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Record W4415762809 · doi:10.1038/s44168-025-00307-5

From pledges to places: action agendas need spatial data to integrate climate and biodiversity action

2025· letter· en· W4415762809 on OpenAlexafffund
Paul Hagenström, Nathalie Pettorelli, Idil Boran, Peter Bridgewater, Deborah Delgado Pugley, Hollie Folkard‐Tapp, Angel Hsu, Pablo Imbach, Marcel Kok, Stacy D. VanDeveer, Oscar Widerberg, Sander Chan

Bibliographic record

Venuenpj Climate Action · 2025
Typeletter
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsInstitute for Work & HealthCentre for Global Health ResearchYork University
FundersHORIZON EUROPE Framework ProgrammeUK Research and InnovationDeutsche ForschungsgemeinschaftEconomic and Social Research CouncilEuropean Climate, Infrastructure and Environment Executive AgencyEuropean CommissionGovernment of Canada
KeywordsAction (physics)BiodiversityClimate changeCitizen journalismSpatial planningAdaptation (eye)Global warming

Abstract

fetched live from OpenAlex

As COP30 approaches, policymakers must ensure that the integration of climate and biodiversity action by non-state and subnational actors is anchored in spatial data. Otherwise, we cannot see where change is happening, how effective it is, or who bears costs and benefits. The UNFCCC Global Climate Action and CBD Action Agenda Portals should lead by requiring spatial details on implementation, enabling more credible and participatory monitoring, analysis, and collaboration.

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)0.015
metaresearch head score (Gemma)0.041
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.067
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.007
Scholarly communication0.0080.015
Open science0.0030.007
Research integrity0.0670.055
Insufficient payload (model declined to judge)0.0200.013

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 head0.131
GPT teacher head0.319
Teacher spread0.189 · 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
GenreCommentary

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

Citations1
Published2025
Admission routes2
Has abstractyes

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