MétaCan
Menu
← Back to cohort
Record W4404142860 · doi:10.5558/tfc2024-030

Future research plans to support forest carbon policy in Canada

2024· article· en· W4404142860 on OpenAlexaffvenueabout
C. Smyth, Kara L. Webster, Céline Boisvenue, H.F. Macdonald, Sophie Le Noble, Jessica Grenke, John Ford, M.B. Kicknosway

Bibliographic record

VenueThe Forestry Chronicle · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsCarbon fibersEnvironmental resource managementNatural resource economicsEnvironmental planningEnvironmental sciencePolitical scienceBusinessEconomicsComputer science

Abstract

fetched live from OpenAlex

This article summarizes a ten-year plan for forest carbon science that was developed in a collaborative effort with the forest carbon science and policy community in Canada. Building on the research progress since the first plan, the updated Blueprint outlines key priorities, goals, and visions for forest carbon research over the next decade. Here we describe the five essential research areas, namely: A) understanding human impacts on forest carbon; B) exploring foundational forest carbon dynamics; C) assessing climate change mitigation strategies; D) promoting reconciliation and including Indigenous Knowledges in meaningful and authentic ways; and E) contextualizing carbon within the broad range of forest values. The Blueprint serves as a guide for the development of research supporting policies that continue to foster sustainable forest management and maintain and enhance collaborative carbon research in Canada.

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)0.016
metaresearch head score (Gemma)0.019
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: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.853

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0150.003
Scholarly communication0.0110.004
Open science0.0040.005
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0240.002

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 head0.019
GPT teacher head0.291
Teacher spread0.273 · 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
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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueThe Forestry Chronicle→Same topicForest Management and Policy→French-language works237,207→