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Record W2600938328

Climate Change and Marine Transportation on the St. Lawrence River. Exploratory Study of Adaptation Option

2005· article· en· W2600938328 on OpenAlexaboutno aff
Paul D’Arcy, J-F. Bibeault, R. B. Raffa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeAdaptation (eye)Perspective (graphical)Sustainable developmentEnvironmental resource managementExploratory researchEnvironmental planningContemplationPosition (finance)Political scienceBusinessGeographySociologyComputer scienceEconomicsPsychologySocial science
DOInot available

Abstract

fetched live from OpenAlex

Thus, in an exploratory manner and with the objective of initiating an examination and contemplation of climate change adaptation measures, a few adaptation options were reviewed and, where possible, their economic, environmental and social impacts considered. This research made it possible to uncover certain limitations and opportunities specific to each option and to illustrate the complexity of their analysis on the basis of sustainable development criteria. Additional studies would therefore be necessary to be in a position to effectively determine those that are in line with this perspective and to discover new ones. A number of initiatives are emerging both in Canada and abroad so that adaptations to climate change be taken into consideration now. The impacts of climate change go beyond the framework of navigation activities alone, and the Navigation Consensus Building Committee believes that, in the coming years, an examination of the future vocation of the St. Lawrence from the perspective of sustainable development and a more integrated management must be undertaken.

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 score0.409
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.104
GPT teacher head0.320
Teacher spread0.217 · 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
Published2005
Admission routes1
Has abstractyes

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