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Record W2212754292 · doi:10.1670/195-03a

Using Chorus-Size Ranks from Call Surveys to Estimate Reproductive Activity of the Wood Frog (Rana sylvatica)

2004· article· en· W2212754292 on OpenAlexaffabout
Cameron E. Stevens, Cynthia A. Paszkowski

Bibliographic record

VenueJournal of Herpetology · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyChorusPopulationEcologyZoologyRank (graph theory)Sampling (signal processing)Reproductive biologyDemographyFisheryMathematics

Abstract

fetched live from OpenAlex

Call surveys are a relatively new and efficient technique for detecting the presence of breeding male anurans. Using data from multiple surveys of breeding choruses of Wood Frogs (Rana sylvatica) combined with counts of egg masses on ponds in west-central Alberta we determined (1) the relationship between ranks of chorus size and total number of egg masses in ponds, and (2) number of breeding males in standard chorus-size ranks (1, 2 and 3). Estimates for Rank 3 choruses were based on a formula with number of egg masses present per pond and a fixed male to female ratio of 2:1 calculated from the literature. Calling males were recorded from all ponds that had evidence of female reproductive activity (i.e., egg masses). Generalized linear models suggested that ranks were positively and linearly correlated with the number of egg masses in a pond. In addition, call data from only the second of four sampling periods (each 3–6 days) significantly predicted number of egg masses in ponds, suggesting that timing is important when surveying calling wood frogs. The mean number of chorusing males per rank did not correspond to aural ranks of calling intensity: Rank 1 = 1.3 males, Rank 2 = 3.7 males, and Rank 3 = 118 males. We recommend similar assessments for other widely distributed species to improve our ability to detect and interpret habitat-use patterns and population trends of amphibians through monitoring programs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.042
GPT teacher head0.348
Teacher spread0.307 · 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 designBench or experimental
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

Citations42
Published2004
Admission routes2
Has abstractyes

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