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Record W1844273213 · doi:10.1139/z06-146

The significance of hydroperiod and stand maturity for pool-breeding amphibians in forested landscapes

2006· article· en· W1844273213 on OpenAlexvenueno aff
Robert F. Baldwin, Aram J. K. Calhoun, Phillip deMaynadier

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
FundersRobert and Patricia Switzer Foundation
KeywordsAmphibianSalamanderBiologyEcologyHabitatWetlandCaudataSeasonal breeder

Abstract

fetched live from OpenAlex

The loss of small seasonal wetlands and adjacent forested habitat is a major threat to pool-breeding amphibians in North America. Identifying environmental correlates of breeding effort (and success) in remaining intact landscapes is a critical first step in conservation planning. Little is known about how pool-breeding amphibian populations respond to fine-scale variations in hydroperiod or neighboring forest structure and composition. We studied these associations for wood frogs ( Rana sylvatica LeConte, 1825) and spotted salamanders ( Ambystoma maculatum (Shaw, 1802)) in a forested New England landscape (southern Maine, USA). We conducted egg mass counts across two seasons at 87 strictly seasonal pools. The influence of hydroperiod and landscape (150 and 500 m scales) habitat characteristics on breeding effort were investigated. Pools with longer hydroperiods (≥18 weeks post breeding) that were relatively isolated from other breeding wetlands (<13 neighboring pools within 150 m and <19 within 500 m) supported larger breeding populations of both wood frogs and spotted salamanders. Salamander breeding populations were largest in relatively mature forests. Naturalized, anthropogenic pools supported comparable levels of breeding effort with that of natural pools. Conservation planning for wood frogs and spotted salamanders should incorporate pools at the longer end of the seasonal hydroperiod gradient.

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 score0.490
Threshold uncertainty score0.858

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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations84
Published2006
Admission routes1
Has abstractyes

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