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Record W2146626298 · doi:10.1139/z07-046

Life-history traits in green toad (<i>Bufo viridis</i>) populations: indicators of habitat quality

2007· article· en· W2146626298 on OpenAlexvenueno aff
Ulrich Sinsch, Christoph Leskovar, Anja Drobig, Astrid König, Wolf-Rüdiger Große

Bibliographic record

VenueCanadian Journal of Zoology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyLongevityHabitatEcologyLife history theoryZoologyBufoSexual maturityAltitude (triangle)Life historyToad

Abstract

fetched live from OpenAlex

Five life-history traits (age and size at maturity, longevity, potential reproductive life span, age-dependent growth rate) were investigated in four Bufo viridis Laurenti, 1768 (= Pseudepidalea viridis (Laurenti, 1768)) populations that inhabited localities at similar altitude (60–100 m above sea level) and latitude (50°N–51°N, Germany), but that differed in habitat quality (i.e., human land use within a radius of 1 km around the breeding site). The age of 374 males and of 127 females collected during the breeding period was estimated using skeletochronology on phalange bones. We tested the hypothesis that sex and habitat quality account for detectable amounts of local variation in life-history traits. Significant sexual size dimorphism was present in all populations. Gender-specific variation in size was mainly accounted for by age, but also to a minor extent by habitat quality. In males, age at maturity varied between 1 and 3 years and was the only life-history trait that was significantly related to the intensity of human land use. In contrast, land-use indices covaried significantly with female longevity (6–15 years) and potential reproductive life span (5–12 years). Our pilot study suggests that, in B. viridis, life-history traits derived from the local age structure may be useful as indicators of habitat quality.

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 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 score0.561
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.251
Teacher spread0.226 · 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

Citations60
Published2007
Admission routes1
Has abstractyes

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