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  "name": "sites-stories-interactive-for-crc-paper",
            
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  "notes": "Results: Community-Informed Climate Impact Assessment of 
16
  "notes": "Results: Community-Informed Climate Impact Assessment of 
            
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Nunapitchuk, Alaska\r\n\r\nThis is a preliminary analysis and 
17
Nunapitchuk, Alaska\r\n\r\nThis is a preliminary analysis and 
            
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interactive site combining results of Arctic Infrastructure Analyses 
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interactive site combining results of Arctic Infrastructure Analyses 
            
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that link narrative interview information with on the ground 
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that link narrative interview information with on the ground 
            
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environmental information collected using drone sensors. The study 
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environmental information collected using drone sensors. The study 
            
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presents a mixed-methods analysis integrating stakeholder interviews 
21
presents a mixed-methods analysis integrating stakeholder interviews 
            
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(n=7, ages 43-80) with geospatial data to assess climate-induced 
22
(n=7, ages 43-80) with geospatial data to assess climate-induced 
            
23
infrastructure vulnerabilities in Nunapitchuk, Alaska. Through 
23
infrastructure vulnerabilities in Nunapitchuk, Alaska. Through 
            
24
systematic coding of interview transcripts, we identified 47 
24
systematic coding of interview transcripts, we identified 47 
            
25
references to permafrost degradation, representing the dominant 
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references to permafrost degradation, representing the dominant 
            
26
environmental concern across all stakeholder groups. The analysis 
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environmental concern across all stakeholder groups. The analysis 
            
27
revealed critical infrastructure failures affecting 38 documented 
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revealed critical infrastructure failures affecting 38 documented 
            
28
structures, with differential subsidence rates showing south-facing 
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structures, with differential subsidence rates showing south-facing 
            
29
foundations degrading 40% faster than north-facing 
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foundations degrading 40% faster than north-facing 
            
30
structures.\r\n\r\nOur Scientific Variable Object (SVO) framework 
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structures.\r\n\r\nOur Scientific Variable Object (SVO) framework 
            
31
mapped nine quantifiable metrics to stakeholder priorities, revealing 
31
mapped nine quantifiable metrics to stakeholder priorities, revealing 
            
32
high concordance (>80%) between elder traditional knowledge and 
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high concordance (>80%) between elder traditional knowledge and 
            
33
scientific measurements for permafrost thaw rates and ground 
33
scientific measurements for permafrost thaw rates and ground 
            
34
subsidence indicators. Water quality assessments detected arsenic 
34
subsidence indicators. Water quality assessments detected arsenic 
            
35
concentrations downstream from waste facilities, corroborating 
35
concentrations downstream from waste facilities, corroborating 
            
36
community-reported health concerns including unprecedented cancer 
36
community-reported health concerns including unprecedented cancer 
            
37
clusters and first-time waterborne illnesses among lifelong 
37
clusters and first-time waterborne illnesses among lifelong 
            
38
residents.\r\n\r\nThe perceived environmental change trajectory, 
38
residents.\r\n\r\nThe perceived environmental change trajectory, 
            
39
validated through temporal analysis spanning 1920-2024, demonstrates 
39
validated through temporal analysis spanning 1920-2024, demonstrates 
            
40
accelerating degradation with ground stability declining from 95% 
40
accelerating degradation with ground stability declining from 95% 
            
41
(1970s) to 30% (2020s). Multiple stakeholders independently estimated 
41
(1970s) to 30% (2020s). Multiple stakeholders independently estimated 
            
42
a 3-5 year window before complete infrastructure failure, aligning 
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a 3-5 year window before complete infrastructure failure, aligning 
            
43
with geotechnical projections showing active layer depths increasing 
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with geotechnical projections showing active layer depths increasing 
            
44
at 12cm/year.\r\n\r\nEconomic impact analysis revealed a 900% increase 
44
at 12cm/year.\r\n\r\nEconomic impact analysis revealed a 900% increase 
            
45
in energy costs over one generation ($20/barrel to $190/month), 
45
in energy costs over one generation ($20/barrel to $190/month), 
            
46
threatening community sustainability. The 3D point cloud visualization 
46
threatening community sustainability. The 3D point cloud visualization 
            
47
captured 2.3km\u00b2 of riverbank terrain at 15-point/m\u00b2 density, 
47
captured 2.3km\u00b2 of riverbank terrain at 15-point/m\u00b2 density, 
            
48
documenting erosion rates of 2.5m/year and permafrost-related surface 
48
documenting erosion rates of 2.5m/year and permafrost-related surface 
            
49
deformation patterns.\r\n\r\nCritical findings indicate immediate 
49
deformation patterns.\r\n\r\nCritical findings indicate immediate 
            
50
federal intervention is required, with $11 million already allocated 
50
federal intervention is required, with $11 million already allocated 
            
51
for waste management upgrades. The convergence of traditional 
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for waste management upgrades. The convergence of traditional 
            
52
knowledge with quantitative measurements strengthens the validity of 
52
knowledge with quantitative measurements strengthens the validity of 
            
53
relocation timelines. This integrated assessment framework provides a 
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relocation timelines. This integrated assessment framework provides a 
            
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replicable methodology for climate impact evaluation in Arctic 
54
replicable methodology for climate impact evaluation in Arctic 
            
55
communities, demonstrating the value of incorporating indigenous 
55
communities, demonstrating the value of incorporating indigenous 
            
56
knowledge systems into engineering assessments for infrastructure 
56
knowledge systems into engineering assessments for infrastructure 
            
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resilience planning.\r\n\r\nComparison: AI vs Human Coding AnalysisKey 
57
resilience planning.\r\n\r\nComparison: AI vs Human Coding AnalysisKey 
            
58
Differences: \r\n\r\nThe AI analysis identified significantly more 
58
Differences: \r\n\r\nThe AI analysis identified significantly more 
            
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thematic references (215 total) compared to human coding (19 entries), 
59
thematic references (215 total) compared to human coding (19 entries), 
            
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with AI emphasizing permafrost degradation as the primary concern 
60
with AI emphasizing permafrost degradation as the primary concern 
            
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while human coders focused more granularly on specific infrastructure 
61
while human coders focused more granularly on specific infrastructure 
            
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issues like boardwalks and dump management. Human coding showed the 
62
issues like boardwalks and dump management. Human coding showed the 
            
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City Manager contributing 58% of coded entries, suggesting more 
63
City Manager contributing 58% of coded entries, suggesting more 
            
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detailed attention to administrative perspectives, whereas AI analysis 
64
detailed attention to administrative perspectives, whereas AI analysis 
            
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distributed concerns more evenly across stakeholder groups. The AI 
65
distributed concerns more evenly across stakeholder groups. The AI 
            
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approach captured broader thematic patterns and frequencies, while 
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approach captured broader thematic patterns and frequencies, while 
            
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human coding provided more precise, quote-specific categorizations 
67
human coding provided more precise, quote-specific categorizations 
            
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with detailed contextual understanding. Both approaches identified 
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with detailed contextual understanding. Both approaches identified 
            
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waste management and infrastructure failure as critical issues, but 
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waste management and infrastructure failure as critical issues, but 
            
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differed in their emphasis on cultural preservation (AI) versus 
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differed in their emphasis on cultural preservation (AI) versus 
            
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practical infrastructure solutions (human coders).Similarities: Both 
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practical infrastructure solutions (human coders).Similarities: Both 
            
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identified dump/waste management and infrastructure problems related 
72
identified dump/waste management and infrastructure problems related 
            
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to ground instability as critical issues, recognized water quality 
73
to ground instability as critical issues, recognized water quality 
            
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concerns, and captured the urgency of the situation requiring 
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concerns, and captured the urgency of the situation requiring 
            
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immediate intervention.",
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