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Record W4200025067 · doi:10.3996/jfwm-21-011

Efficacy of Visual Encounter Surveys for Coastal Tailed Frog Detection

2021· article· en· W4200025067 on OpenAlexaff
Ben Millard‐Martin, Melissa Todd, Chris J. Johnson, Alexandria L. McEwan

Bibliographic record

VenueJournal of Fish and Wildlife Management · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsMinistry of ForestsUniversity of Northern British Columbia
Fundersnot available
KeywordsHabitatEcologySTREAMSWatershedSampling (signal processing)Environmental scienceGeographyElevation (ballistics)Hydrology (agriculture)Physical geographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Coastal tailed frogs Ascaphus truei inhabit montane streams and forested habitats in the Coast and Cascade mountains from northern California to the Skeena River watershed in northwestern British Columbia. Terrestrial adults and juveniles of this cryptic biphasic species are difficult to survey as they are small, do not vocalize, and may be associated with woody ground structures or subsurface refugia at considerable distances from natal streams. We performed a comparative analysis of the detection rate of postmetamorphic coastal tailed frogs and ecological factors hypothesized to influence detection when conducting visual encounter and pitfall trap surveys. We conducted concurrent surveys in northwestern British Columbia at six sites over similar time periods using both techniques. The average detection rate of visual encounter surveys (mean = 0.249, SD = 0.702) was greater than that of pitfall sampling (mean = 0.138, SD = 0.773) when cool temperatures and high humidity favor aboveground movement during the daytime. Light-touch ground searches of refuge habitats likely enhanced detection during visual surveys. Although the average detection rate was less, pitfall traps provided 24-h sampling and were less affected by the experience of the surveyor and the occurrence of ground cover. In general, variation in seasonal behavior influenced detection regardless of method. The relatively higher cost and fixed nature of pitfall traps should be weighed against the ability to apply more cost-effective visual encounter surveys to a greater number of sites.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.220

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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