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Record W2099765793 · doi:10.1139/z00-043

Demographic analysis of the Columbia spotted frog (<i>Rana luteiventris</i>): case study in spatiotemporal variation

2000· article· en· W2099765793 on OpenAlexvenueno aff
Jamie K. Reaser

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEcologyAmphibianPredationPopulationDemographicsSurvivorship curveRange (aeronautics)DemographyZoology

Abstract

fetched live from OpenAlex

This study examined size, mass, sex ratios, and age structure of the Columbia spotted frog (Rana luteiventris) at seven sites in the Toiyabe Range in central Nevada from 1994 through 1996. Age structure was estimated using skeletochronology. Demographic composition was found to be significantly different among sites, suggesting that localized factors influence recruitment and mortality rates. Demographics among years at the sites were also significantly different, indicating that the population dynamics of this system are complex and are also driven by one or more temporal factors. Knowledge of local land-use patterns and anecdotal observations were incorporated in an attempt to identify potential stress agents in need of further research and possible intensive management. Differences in recruitment, survivorship, and mortality rates among sites may be due to microclimate, food availability, and predation rates. Introduction of exotic trout and cattle are likely the most important anthropogenic factors limiting the distribution and persistence of R. luteiventris in the study area. Extreme variations in annual weather patterns may account for many differences observed at some sites. This study demonstrates that adequate assessment of amphibian population status requires knowledge of subpopulation demographics across a broad landscape.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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.

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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