MétaCan
Menu
← Back to cohort
Record W7096286805

Fecundity determines the extinction threshold in a Canadian assemblage of longhorned beetles (Coleoptera: Cerambycidae)

2004· article· en· W7096286805 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatExtinction (optical mineralogy)Extinction debtFecundityHabitat fragmentationHabitat destructionFragmentation (computing)
DOInot available

Abstract

fetched live from OpenAlex

Reproductive rate has been suggested to have a positive effect on the amount of habitat loss a species can tolerate while emigration from habitat patches has been suggested to have both positive and negative effects. Forest fragmentation has been suggested to have negative effects on forest species. We determined the extinction threshold for 12 species of saproxylic (dead wood dependent) longhorned beetles (Coleop-tera: Cerambycidae) using trap catch data from Ontario, Canada. We also determined the maximum egg production of each species and whether they were likely to move outside of forest patches. We found a strong negative relationship between reproductive rate and the minimum habitat amount required for species presence. This relationship is obscured if the scale of investigation is not appropriate for the study organism. As well, species caught moving outside forest habitat had lower extinction thresholds than species not caught moving outside forest but this was not significant after accounting for reproductive rate. Fragmentation did not have an effect on the minimum habitat requirements. These relationships can inform predictions of which species will be most affected by habitat loss.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.208
Teacher spread0.188 · 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 designObservational
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
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicForest Ecology and Biodiversity Studies→French-language works237,207→