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Record W2159589879 · doi:10.1139/z05-182

Gorging on gobies: beneficial effects of alien prey on a threatened vertebrate

2006· article· en· W2159589879 on OpenAlexvenueaboutno aff
Richard B. King, Julie M. Ray, Kristin M. Stanford

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersU.S. Fish and Wildlife Service
KeywordsRound gobyBiologyNeogobiusPredationThreatened speciesEndangered speciesEcologyInvasive speciesPopulationIntroduced speciesZoologyHabitat

Abstract

fetched live from OpenAlex

Invasive species often have rapid and far-reaching negative impacts on populations and ecological communities. These effects are most common when invasive species have few competitors or predators. Although higher level carnivores do consume invasive species, quantitative effects of new and abundant food sources on predators have rarely been documented and, as a consequence, potentially positive effects of invasive species may be under appreciated. We investigated the effects of the invasive round goby (Neogobius melanostomus (Pallas, 1814)) on diet composition, growth rate, and body size of the Lake Erie Water Snake (Nerodia sipedon insularum (Conant and Clay, 1937)), which is threatened in the USA and endangered in Ontario, Canada. Water Snakes have shifted their diet, and round gobies now constitute >92% of prey consumed. This shift in diet has occurred in just one or two Water Snake generations, yet has resulted in more rapid growth and attainment of larger body size in Water Snakes. These positive effects may reduce predation, speed reproductive maturity, increase offspring production, and fuel population growth of this threatened species.

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 score0.011
Threshold uncertainty score0.023

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations145
Published2006
Admission routes2
Has abstractyes

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