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Record W2318737054 · doi:10.1139/z2012-079

Natural and anthropogenic substrates affect movement behavior of the Southern Graycheek Salamander (<i>Plethodon metcalfi</i>)

2012· article· en· W2318737054 on OpenAlexvenueno aff
Raymond D. Semlitsch, Stephen M. Ecrement, Andrea Fuller, K.L. Hammer, Jeffrey T. Howard, C. Krager, J. Mozeley, Jonathan Ogle, Natasha Marie Shipman, Jackie Speier, Mark Walker, Bradford L. Walters

Bibliographic record

VenueCanadian Journal of Zoology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersUniversity of Missouri
KeywordsEcologySalamanderSubstrate (aquarium)BiologyPopulationHabitatPlant litterEcosystem

Abstract

fetched live from OpenAlex

Movement behavior is a critical process that interacts with landscape structure to affect population connectivity and persistence in fragmented or altered landscapes. The purpose of our study was to test whether different substrates (forest litter, soil, grass, gravel, and asphalt) found in fragmented forested landscapes affected the movement behavior of the Southern Graycheek Salamander (Plethodon metcalfi Brimley, 1912). Latency period of the salamanders was highest on grass substrate and significantly lower only on soil substrate. Sinuosity of the movement path of salamanders was lowest and contained more turns in grass and was significantly higher than only gravel and asphalt substrates. Velocity of the salamanders was highest on asphalt substrate but was not different from gravel substrate. Velocity was higher on asphalt than on grass, forest, or soil, and velocity was higher on gravel than on grass substrate. The results indicated that P. metcalfi reacted differently to natural and anthropogenic substrates, and we suggest that these behavioral differences could have both positive and negative implications for movement success and habitat resistance in forested landscapes fragmented by roads and development.

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 score0.045
Threshold uncertainty score0.998

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)0.0010.000

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 head0.015
GPT teacher head0.240
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.

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

Citations22
Published2012
Admission routes1
Has abstractyes

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