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Record W2792502156 · doi:10.1080/20442041.2018.1427950

A comprehensive evaluation of <i>Daphnia pulex</i> foraging energetics and the influence of spatially heterogeneous food

2018· article· en· W2792502156 on OpenAlexafffund
Audrey Helen Reid, W. Gary Sprules

Bibliographic record

VenueInland Waters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto Mississauga
KeywordsForagingDaphnia pulexZooplanktonDaphniaBiologyPulexEcologyOptimal foraging theoryEnergeticsEnvironmental science

Abstract

fetched live from OpenAlex

Aquatic organisms are distributed in a heterogeneous, nonrandom manner, and zooplankton are known to seek out and orient themselves within regions of high food concentration. Yet, the energetics associated with this foraging behavior are unknown. We hypothesized that zooplankton foraging behavior increases foraging efficiency measured as food consumption per unit effort. In this study, we measured the energetic costs and benefits for the zooplankton Daphnia pulex foraging in a range of algal concentrations to determine the net energetic benefit of foraging in algal patches. The net energy benefit of foraging increased significantly with increases in algal concentration; this trend was driven by significant increases in prey ingestion with algal concentration, whereas foraging costs did not change. Even considering corrections for changes in assimilation efficiency, foraging in algal patches greatly increases net foraging benefit compared to foraging in low concentrations of algae and thus has the potential for higher reproduction and greater growth in filter-feeding zooplankton.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.048
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 teacher head, 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

Citations5
Published2018
Admission routes2
Has abstractyes

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