MétaCan
Menu
← Back to cohort
Record W2181517238

Evaluating the Impacts of Climate Changes on Nunavik Marine Infrastructures and Adaptation Solutions

2012· article· en· W2181517238 on OpenAlexaboutno aff
Yann Ropars

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsStorm surgeClimate changeContext (archaeology)BreakwaterStormEnvironmental scienceVulnerability (computing)Sea iceEffects of global warmingGeographyGlobal warmingEnvironmental resource managementClimatologyOceanographyMeteorologyGeologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Since 1999, fourteen marine infrastructures have been built in the northern villages of Nunavik, Quebec. These infrastructures generally consist in breakwaters, concrete wharves, access ramps and floating pontoons. During the design of these infrastructures, no consideration was given to potential climate change influence on specific design parameters. Neither data nor projections were available at the time. Transports Quebec, in collaboration with Kativik Regional Government, Ouranos and CIMA+ then developed a project aiming at evaluating the marine infrastructure vulnerability in a context of climate change in order to identify adaptation solutions and ensure these infrastructures will last and stay safe to use. In order to anticipate potential impacts of climate changes on marine infrastructures, storm tracks, water levels, waves, sea and coastal ice are being modelled in the Nunavik region. Climate change will impact the way storms travel. Storms can occasionally produce large waves and severe surges that both may impact the infrastructures in several ways. The longer ice-free season increases the probability for waves and storm surges to develop. The reduced stability of the ice cover increases the risk of ice-related damage on the marine infrastructures. Different models (sea ice, storm surge, waves) are being tested and validated in order to check the influence of the different storm track patterns and the run. Integration of all this information in a comprehensive marine infrastructure vulnerability evaluation model will definitely be a challenge, but the first results are quite promising.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.285
Teacher spread0.239 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicArctic and Antarctic ice dynamics→French-language works237,207→