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Record W4406675813 · doi:10.56367/oag-045-11366

Helping biodiversity conservation with modelling

2025· article· en· W4406675813 on OpenAlexaffabout
Guillaume Blanchet

Bibliographic record

VenueOpen Access Government · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBiodiversityBiodiversity conservationEnvironmental resource managementEnvironmental planningGeographyEnvironmental scienceBusinessEcologyBiology

Abstract

fetched live from OpenAlex

Helping biodiversity conservation with modelling Professor Guillaume Blanchet from Université de Sherbrooke discusses how modelling can aid in the conservation of biodiversity. The copper redhorse is a fish species found only in a few rivers near and around Montréal (Québec, Canada). The only place it is known to spawn is in the Richelieu River, commonly used for recreational activities (e.g. sport fishing and waterskiing). In short, the copper redhorse lives in an area where there is a lot of human activity. As far as we know, it would be surprising if more than a thousand adults of the copper redhorse existed. Needless to say, the copper redhorse is not doing well; it is endangered of extinction.

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)0.010
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0070.012
Open science0.0030.009
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0670.023

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 head0.044
GPT teacher head0.287
Teacher spread0.243 · 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 designSimulation or modeling
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
Published2025
Admission routes2
Has abstractyes

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