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Record W3164704062 · doi:10.1126/sciadv.abd6034

Natural climate solutions for Canada

2021· article· en· W3164704062 on OpenAlexafffundabout
C. Ronnie Drever, Susan C. Cook‐Patton, Fardausi Akhter, Pascal Badiou, Gail L. Chmura, Scott J. Davidson, R. L. Desjardins, A. Dyk, Joseph Fargione, Max Fellows, Ben Filewod, Margot Hessing‐Lewis, Susantha Jayasundara, William S. Keeton, Timm Kroeger, Tyler J. Lark, Edward Le, Sara M. Leavitt, Marie-Eve LeClerc, Tony C. Lemprière, Juha M. Metsaranta, B.G. McConkey, Eric T. Neilson, Guillaume Peterson St‐Laurent, Danijela Puric-Mladenovic, Sébastien Rodrigue, Raju Soolanayakanahally, S. Spawn, Maria Strack, C. Smyth, Naresh V. Thevathasan, Mihai Voicu, C. A. Williams, Peter B. Woodbury, Devon E. Worth, Zhen Xu, Samantha Yeo, Werner A. Kurz

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaUniversity of VictoriaMcGill UniversityDucks Unlimited CanadaUniversity of British ColumbiaUniversity of GuelphAgriculture and Agri-Food CanadaUniversity of WaterlooUniversity of TorontoCanadian Forest ServiceAssembly of First Nations
FundersTula FoundationEcho FoundationRoyal Bank of CanadaWilliam and Flora Hewlett FoundationGeorge Cedric Metcalf Charitable Foundation
KeywordsEnvironmental scienceProductivityGreenhouse gasClimate change mitigationGrasslandBiodiversityEnvironmental resource managementClimate changeEnvironmental protectionNatural resource economicsBusinessEcology

Abstract

fetched live from OpenAlex

e. Avoided conversion of grassland, avoided peatland disturbance, cover crops, and improved forest management offer the largest mitigation opportunities. The mitigation identified here represents an important potential contribution to the Paris Agreement, such that NCS combined with existing mitigation plans could help Canada to meet or exceed its climate goals.

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)0.002
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 score0.102
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.000
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1020.014

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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

Citations213
Published2021
Admission routes3
Has abstractyes

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Same venueScience AdvancesSame topicFire effects on ecosystemsFrench-language works237,207