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Climate Adaptation/Mitigation

2019· other· en· W2910160147 on OpenAlexaff
Gordon McBean

Bibliographic record

VenueInternational Encyclopedia of Geography · 2019
Typeother
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsWestern University
Fundersnot available
KeywordsAdaptation (eye)Disaster risk reductionClimate changeSustainable developmentEnvironmental resource managementEnvironmental planningUnited Nations Framework Convention on Climate ChangeSocioeconomic statusBusinessClimate change adaptationClimate change mitigationEnvironmental economicsNatural resource economicsRisk analysis (engineering)Environmental sciencePolitical scienceEconomicsEcology

Abstract

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The climate is changing due to humans' actions and humans need to respond through mitigation – emission reductions – to reduce the rate of change and through adaptation to reduce the impacts and gain the benefits. The Climate Convention, with the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals, provides an international framework for both mitigation and adaptation, but countries need to develop and implement national strategies on the basis of scientific‐technological and socioeconomic information and analyses and their national to local capabilities.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.078
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0780.032

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 head0.030
GPT teacher head0.295
Teacher spread0.265 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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