MétaCan
Menu
Back to cohort
Record W7099773523

Steering Committee: Norm Catto

2011· article· en· W7099773523 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeNova scotiaCoastal zoneNorm (philosophy)Government (linguistics)Climate change adaptationSea level riseGlobal warming
DOInot available

Abstract

fetched live from OpenAlex

The coastal zone is recognized as a particularly sensitive environment to projected future climate change due to global warming. This includes sensitivity to increases in air, sea and ground temperatures; variations in the frequency of and intensity of storms; variations in sea and lake levels; variations in amounts, patterns, and styles of precipitation; and changes in sea ice extent, duration, and thickness; these changes are likely to affect coastal structures and a wide-variety of human activities. The special sensitivity of the coastal zone to climate change impacts has prompted the Government of Canada to establish a “Coastal Node ” as part of the Canadian Climate Impact and Adaptation Network (C-CIARN). A workshop with a broad representation of stakeholders from all coastal regions of Canada was held in Dartmouth, Nova Scotia in March 2001 to outline the role of a “Coastal Node”, identify a range of sensitive coastal resources and associated climate change issues, and provide guidelines for research priorities; this report summarizes the results of that workshop. C-CIARN The discussion of the C-CIARN Coastal Node was predicated on the understanding that Natural Resources Canada (NRCan) would provide some funding for a Coastal Node Coordinator, who

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.037
GPT teacher head0.195
Teacher spread0.159 · 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; both teacher heads agree on what is shown here.

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
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicOcean Acidification Effects and ResponsesFrench-language works237,207