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Record W2165902876 · doi:10.1139/cjfas-2015-0026

Are small fishes more sensitive to habitat loss? A generic size-based model

2015· article· en· W2165902876 on OpenAlexafffundvenue
Adam S. van der Lee, Marten A. Koops

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsHabitatEcologyJuvenileHabitat destructionBiologyBiodiversityPopulationDemography

Abstract

fetched live from OpenAlex

Habitat loss represents the greatest threat to freshwater biodiversity. The potential for life history attributes to correlate with the risks associated with habitat loss represents a possible mechanism for more effective and rapid assessments, especially in data-limited situations. Body size correlates with many other life history attributes and is a good starting point for investigating correlates with habitat loss. Here, we use a generic stage-based matrix population model, parameterized using length-based allometries, to investigate if such a mechanism exists. Our analysis revealed that small species (shorter maximum body length) were initially more sensitive to the loss of habitat. Moreover, distinct stage-based patterns exist showing an increased sensitivity of population growth rate for small species to both habitat loss and vital rate perturbations of pre-adult stages. This indicates that the pre-adult period represents a critical stage for the continued production of small species and increased importance of the conservation of habitat used by young-of-the-year and juvenile fishes.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.217
Teacher spread0.179 · 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

Citations26
Published2015
Admission routes3
Has abstractyes

Explore more

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