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Record W2798096542 · doi:10.1139/as-2017-0024

Reduced sea ice protection period increases storm exposure in Kivalina, Alaska

2018· article· en· W2798096542 on OpenAlexvenueaboutno aff
Zhanpei Fang, Patrick T. Freeman, Christopher B. Field, Katharine J. Mach

Bibliographic record

VenueArctic Science · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersBureau of Indian AffairsU.S. Army Corps of EngineersAlexander von Humboldt-Stiftung
KeywordsStormEnvironmental scienceFlooding (psychology)ArcticErosionOceanographyClimatologyStorm surgeGeology

Abstract

fetched live from OpenAlex

On Arctic coasts, erosion is limited by the presence of nearshore sea ice, which creates a protective barrier from storms. In Kivalina, an Alaskan Inupiaq Inuit community, decreasing seasonal sea ice extent and a lengthening of the open-water season may be resulting in fall storms that (1) generate higher, longer, and more destructive waves and (2) cause damage later in the year, resulting in increased flooding and erosion. We assess trends in the duration of nearshore sea ice and their relationship with storm occurrence over the period 1979–2015 in Kivalina. Analysis of passive microwave sea ice concentration data indicates that the open-water season has increased by 5.6 ± 1.2 days/decade over the last 37 years, with moderate evidence that it is extending further into the fall than into the spring. This is correlated with an increased reporting frequency of high-damage storms; 80% of reported storms since 1970 occurred in the last 15 years. Each high-damage storm event occurred during the open-water season for that year. Our findings support Kivalina villagers’ assertions that climate change increases storm exposure and associated damages from flooding and erosion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.227
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2018
Admission routes2
Has abstractyes

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