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Record W4405898791 · doi:10.5869/fc.2024.v29-1.1

Shelter Use in Cambarus robustus, a Surrogate Species for the Federally Listed Cambarus callainus and Cambarus veteranus

2024· article· en· W4405898791 on OpenAlexaboutno aff
Hannah H. Holbert, Zachary J. Loughman, Zackary A. Graham

Bibliographic record

VenueFreshwater Crayfish · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyZoology

Abstract

fetched live from OpenAlex

Abstract The Big Water Crayfish, Cambarus robustus, is native to several eastern U.S. states and Ontario, Canada. Cambarus robustus possesses several traits that make it a potential surrogate for federally listed species, such as the Big Sandy Crayfish, Cambarus callainus, and the Guyandotte River Crayfish, Cambarus veteranus. Basic husbandry information, like optimal temperature, and shelter requirements, have yet to be investigated for most Cambarus species. Therefore, to create a protocol for shelter requirements and preferences for federally listed Cambarus species, we conducted a shelter preference study with C. robustus. Thirty-one crayfish were placed in two treatments where opportunities to burrow underneath natural shelters, clear acrylic shelters, or tinted acrylic shelters were presented. Clear and tinted acrylic shelters are beneficial for investigators as they allow for monitoring of behavior and health without disrupting the crayfish. Each of the two trials lasted 24 hours and were video recorded to monitor behavior and shelter use. Our results suggest that C. robustus will use all shelter types, although natural shelters were occupied more frequently than acrylic shelters. Sex and size had minimal or no influence on shelter use. Future studies should continue to investigate husbandry and rearing techniques for Cambarus spp. and other species where propagation and head-starting efforts will commence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.925

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 science0.0000.001
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 head0.023
GPT teacher head0.222
Teacher spread0.199 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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