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Record W6930534243 · doi:10.5281/zenodo.14868107

Data from: Influence of warming on the functional responses of invasive omnivores, Procambarus crayfishes

2025· other· en· W6930534243 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsMcGill University
Fundersnot available
KeywordsCrayfishProcambarus clarkiiTrophic levelOmnivoreMacrophyteInvasive speciesEcosystemSwampRange (aeronautics)Aquatic animal

Abstract

fetched live from OpenAlex

The red swamp crayfish (Procambarus clarkii) and the marbled crayfish (Procambarus virginalis) are congeneric invasive species whose potential impacts in the Great Lakes basin have generated concern. In laboratory experiments, we tested the functional responses of these omnivores to two common food resources, insect larvae (Chironomus bloodworms) and aquatic macrophytes (Eurasian milfoil Myriophyllum spicatum), to gain predictive information on their per capita effects under present (18°C) and projected future (26°C) climate scenarios for the basin. The maximum feeding rate of P. virginalis was higher at 18°C than at 26°C when presented with bloodworms but did not differ between temperatures when presented with macrophytes. By contrast, the feeding rate of P. clarkii did not change with temperature for either food resource. Due to their larger mean size, P. clarkii exhibited higher rates of resource (bloodworm and macrophyte) consumption than P. virginalis at both temperatures. These results suggest that trophic impacts of P. virginalis will dampen with increased warming, whereas P. clarkii will sustain larger impacts irrespective of temperature within the range tested.

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 score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.004

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 head0.058
GPT teacher head0.229
Teacher spread0.171 · 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
Published2025
Admission routes1
Has abstractyes

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