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Guidelines for the use and interpretation of palaeofire reconstructions based on various archives and proxies

2018· article· en· W2810608444 on OpenAlexafffund
Cécile C. Remy, Cécile Fouquemberg, Hugo Asselin, Benjamin Andrieux, Gabriel Magnan, Benoît Brossier, Pierre Grondin, Yves Bergeron, Brigitte Talon, Olivier Blarquez, Lisa Bajolle, Adam A. Ali

Bibliographic record

VenueQuaternary Science Reviews · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité de MontréalNatural Resources CanadaMinistère des Ressources naturelles et des ForêtsUniversité du Québec en Abitibi-TémiscamingueNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à MontréalCanadian Forest Service
FundersFonds de recherche du Québec – Nature et technologiesCentre National de la Recherche ScientifiqueNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsCharcoalPeatProxy (statistics)DendrochronologyTaigaPhysical geographyBorealGeologyArchaeologyFire historyFire regimeEarth scienceGeographyForestryEcologyOceanographyClimate changeEcosystem

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.040
metaresearch head score (Gemma)0.117
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: Methods · Consensus signal: Methods
Teacher disagreement score0.040
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.117
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.009
Science and technology studies0.0030.002
Scholarly communication0.0080.006
Open science0.0090.004
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0230.020

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 head0.067
GPT teacher head0.316
Teacher spread0.249 · 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
GenreMethods

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

Citations67
Published2018
Admission routes2
Has abstractno

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