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Record W4391475343 · doi:10.5962/p.353853

Distribution of, and microhabitat use by, woodland salamanders along forest-farmland edges

2003· article· en· W4391475343 on OpenAlexvenueno aff
Gretchen I. Young, Richard H. Yahner

Bibliographic record

VenueThe Canadian Field-Naturalist · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWoodlandGeographyDistribution (mathematics)EcologyHabitatCaudataLand useAgroforestryForestryEnvironmental scienceBiologyMathematics

Abstract

fetched live from OpenAlex

We examined the distribution of, and microhabitat use by, woodland salamander populations along forest-farmland edges at six sites (each 40 ha) in the Valley and Ridge Province of Pennsylvania (U.S.A.) from September to November 1995 and from March to May 1996.We found 570 salamanders of nine species, with most (90.5%)being Redback Salamanders (Plethodon cinereus).Observed versus expected numbers of salamanders of all species combined differed with distances from edges (P < 0.005); only 64 (11%) were found at the immediate edge (i.e., 5 m into the forest at the forest-farmland edge).This finding is partially related to dryer microclimatic conditions at edges.Two of the sites (BL2 and SH1) with the highest number of Redback Salamanders contained higher density of logs, soil temperatures, and percentage coverage of herbaceous growth in fall, and deeper and higher percentage coverage of leaf litter in spring compared to other sites.Woodland salamanders and other amphibians are of conservation concern because of regional population declines and range reductions.Thus, we recommend that cool, moist microclimatic conditions be preserved along forest-farmland edges (e.g., via retention of shaded logs and spring seeps) whenever possible for the conservation of woodland salamander populations.

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 score0.752
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 studies0.0010.000
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 head0.016
GPT teacher head0.191
Teacher spread0.174 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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