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Record W4288793917 · doi:10.26443/msurj.v16i1.66

Changing Climate Change

2021· article· en· W4288793917 on OpenAlexaff
Maya Willard-Stepan, Allie Fong, Yehia Sabaa

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsMcGill University
Fundersnot available
KeywordsGreenhouse gasClimate changeGlobal warmingNatural resource economicsEcological forecastingReforestationAgricultureEnvironmental scienceEnvironmental resource managementBusinessEnvironmental protectionEnvironmental planningGeographyEconomicsAgroforestryEcology

Abstract

fetched live from OpenAlex

It is well established that global warming surpassing 1.5-2°C above pre-industrial levels will cause irreversible damage to our world. The adverse rise in global temperatures is accelerated by anthropogenic activity such as greenhouse gas emissions and environmental degradation. While certain scenarios have been projected to significantly lower global warming rates, most of these developments will require immediate global top-down policy shifts. Several international treaties and agreements have been created to combat climate change. Nonetheless, these remain ineffective at creating meaningful progress and cast doubt on how realizable a positive climate scenario is. In this review, we analyze how regional policies and actions combat the climate crisis by examining how specific community initiatives impact climate indicators such as reforestation, greenhouse gas emissions reduction, and sustainable agriculture. Our findings conclude that local initiatives have shown more immediate success compared to their global counterparts. Thus, additional locally led climate initiatives is warranted.

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)0.004
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.003

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 head0.334
GPT teacher head0.384
Teacher spread0.050 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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