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Evaluating a predator–prey interaction in the field: the interaction between beetle larvae (predator) and tadpoles (prey)

2006· article· en· W2062884759 on OpenAlexaff
Michael J. Rubbo, Reehan S. Mirza, Lisa K. Belden, J. J. Falkenbach, Sara I. Storrs, Joseph M. Kiesecker

Bibliographic record

VenueJournal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsNipissing University
FundersNational Science Foundation
KeywordsMesocosmPredatorBiologyPredationEcologyLarvaEcosystem

Abstract

fetched live from OpenAlex

Abstract The larval amphibian community of temporary pond ecosystems has served as a model for studies in community ecology, with a majority of this work being conducted in mesocosms. Recent research has suggested that mesocosms may overestimate ecological effects; therefore, experimental studies conducted under field conditions are required to gauge the results of mesocosm studies. To assess a species interaction under more natural conditions, we conducted a series of field experiments examining the predator–prey interaction between beetle larvae ( Dytiscus sp.; predator) and larval wood frogs Rana sylvatica (prey) in central Pennsylvania, USA. Quantitative sampling of woodland ponds indicated that beetle larvae of the genus Dytiscus were the most common predator of tadpoles. In a field enclosure experiment, dytiscids were effective predators of tadpoles in the pond environment. Moreover, tadpoles avoided areas in a pond containing caged dytiscids, demonstrating that tadpoles recognize the chemical stimuli of predators in complex environments. The results of this study are consistent with data from prior laboratory and mesocosm studies and suggest that these venues can produce reliable interpretations of predator–prey dynamics in this community.

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)0.001
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 score0.031
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.024
GPT teacher head0.310
Teacher spread0.287 · 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

Citations20
Published2006
Admission routes1
Has abstractyes

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