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Record W1973734690 · doi:10.1139/f99-289

Role of satiation in the functional response of a piscivore, largemouth bass (<i>Micropterus salmoides</i>)

2000· article· en· W1973734690 on OpenAlexvenueno aff
Timothy E. Essington, James R. Hodgson, James F. Kitchell

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMicropterusPredationPiscivoreBass (fish)BiologyForage fishCentrarchidaeEcologyFisheryZoologyPredator

Abstract

fetched live from OpenAlex

We evaluated whether satiation regulates the predation rates of a piscivore, largemouth bass (Micropterus salmoides), in natural settings. A functional response model indicated that predation rates can be reduced by satiation when mean prey density is high or when prey encounters are highly patchy. We then used bioenergetics modeling to estimate the predation rates of individual bass in four lakes during a 16-year period and used stomach content mass in diet samples to evaluate the variability in daily predation rates. Predation rates, expressed as the proportion (p) of the maximum daily consumption rate, were low (mode = 0.3, mean = 0.4). Stomach fullness (s), expressed as the proportion of the stomach fullness associated with the maximum sustainable consumption rate, was highly variable, and 13% of all bass diets had s > 1, indicating that bass could opportunistically forage at rates exceeding their maximum sustainable rate. The low predation rates and the ability to consume prey at rates exceeding the maximum sustainable rate make it unlikely that satiation was an important constraint on bass predation rates. Thus, satiation effects widely represented in modeling studies may be a rare component in piscivore-prey interactions, while prey behavior may be a more important component governing predation rates.

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 score0.007
Threshold uncertainty score0.014

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 head0.009
GPT teacher head0.184
Teacher spread0.175 · 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

Citations40
Published2000
Admission routes1
Has abstractyes

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