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Record W2167627911 · doi:10.1656/058.014.0218

Structure and Dynamics of<i>Lithobates sylvaticus</i>(Wood Frog) at the Periphery of Its Range in Missouri

2015· article· en· W2167627911 on OpenAlexaboutno aff
Raymond D. Semlitsch, Dana L. Drake

Bibliographic record

VenueSoutheastern Naturalist · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsLithobatesJuvenileBiologyRange (aeronautics)EcologyPopulationAmphibianZoologyDemography

Abstract

fetched live from OpenAlex

Lithobates (Rana) sylvaticus (Wood Frog) has an extensive distribution primarily in the Appalachian Mountains in the eastern US and throughout Canada and Alaska. However, peripheral populations exist along the southern edge of its range, including in the Ozark regions of Missouri and Arkansas. We present results on the structure and dynamics of 5 Wood Frog populations studied over 4 years (2004–2007) at the edge of the species' range in central Missouri. We used drift fences and pitfall traps surrounding breeding ponds to sample adults and metamorphosing juveniles. We captured breeding males between 7 February and 13 March, and females between 28 February and 16 March. The sex ratio was male-biased (M:F = 2.4), females were larger than males (mean SVL = 61.3 and 52.3 mm, respectively), and the larval period averaged 14 weeks. The metamorphs had a mean SVL of 18.1 mm and varied in number from 0 to 400 individuals per pond per year. The mean juvenile production per female was 8.7 (range = 0–52), and mean survival from egg to juvenile was 1.28% (range = 0–6.08%). Land managers should consider the species' small population sizes, low recruitment, survival rates of terrestrial stages, and the interaction of population dynamics with changing climate conditions when planning for conservation of Wood Frog populations at the periphery of the species' range.

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 score0.036
Threshold uncertainty score0.316

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 studies0.0000.000
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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