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Record W2781009982 · doi:10.1139/cjz-2017-0023

The right choice: predation pressure drives shell selection decisions in the hermit crab <i>Calcinus</i> <i>californiensis</i>

2017· article· en· W2781009982 on OpenAlexvenueno aff
Elsah Arce, Alex Córdoba‐Aguilar

Bibliographic record

VenueCanadian Journal of Zoology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
KeywordsHermit crabPredationBiologyPredatorAnomuraDecapodaEcologySelection (genetic algorithm)CrypsisFisheryCrustaceanZoology

Abstract

fetched live from OpenAlex

Several prey species use refuges to avoid predation. Prey need to abandon and shift between refuges. However, during such shifting, prey can be vulnerable to predators. We hypothesize that predator presence may induce prey to make mistakes in choosing their refuge. We tested this by inducing the hermit crab Calcinus californiensis Bouvier, 1898 to shift to a new empty gastropod shell (three different species: Columbella Lamarck, 1799, Nerita scabricosta Lamarck, 1822, and Stramonita biserialis (Blainville, 1832)) in the absence and presence of Eriphia squamata Stimpson, 1860, which is an efficient shell-crushing natural crab predator. We expected that when a predator was present, hermit crabs would (i) inspect fewer shells and (or) (ii) change to a shell that is either too heavy to allow escape or unfit in size to accommodate the hermit crab. Although the first prediction was met, the second prediction was supported only when S. biserialis shells were used. Thus, in the presence of a predator, hermit crabs prioritize escaping by selecting lighter shells, which would allow the crab to move faster. We conclude that predator presence may induce prey to make mistakes in refuge selection, suggesting that this has severe consequences in future predatory events.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.590
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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