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Record W3039826535 · doi:10.1080/07373937.2020.1786981

Climate change and pandemics: New challenges for science and technology

2020· article· en· W3039826535 on OpenAlexaff
Rubén D. Piacentini, Iván Novara, Arun S. Mujumdar

Bibliographic record

VenueDrying Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsMcGill University
Fundersnot available
KeywordsClimate changePandemicGlobal warmingCoronavirus disease 2019 (COVID-19)Political scienceNatural resource economicsEnvironmental planningEnvironmental scienceEnvironmental resource managementEconomicsEcologyMedicine

Abstract

fetched live from OpenAlex

Global warming is one of the major challenges that needs to be dealt with in the coming decades. In particular, the Intergovernmental Panel on Climate Change (IPCC) published a special report in 20...

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0030.006
Scholarly communication0.0070.016
Open science0.0030.006
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0650.017

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 head0.117
GPT teacher head0.328
Teacher spread0.210 · 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 designTheoretical or conceptual
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

Citations6
Published2020
Admission routes1
Has abstractyes

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