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Record W2896263145 · doi:10.1007/s13358-018-0169-6

Reconstructing predation intensity on crinoids using longitudinal and cross-sectional approaches

2018· article· en· W2896263145 on OpenAlexaff
Tomasz K. Baumiller, Angela Stevenson

Bibliographic record

VenueSwiss Journal of Palaeontology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPredationCrinoidEcologyBiologyPerchPaleontologyFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

Predation has been hypothesized as important to crinoid ecology, and numerous crinoid traits have been linked to predation. However, testing such hypotheses requires some assessment of predation intensity, or pressure. Although direct observations of predatory activity on crinoids are exceedingly rare in the Recent, and unobservable in the fossil record, evidence of predation exists in the form of sublethal damage, especially to their arms. Substantial data exist on the relative frequency, or prevalence, of such injuries, but estimating predation intensity in taxa with ephemeral injuries, such as crinoids, requires combining the prevalence of injuries with rates at which they heal (regenerate). An alternate, independent estimate of predation intensity involves gathering longitudinal data on the number of injuries incurred by particular individuals over a given time span. In this study, predation intensity on crinoids is explored experimentally using these two approaches. We demonstrate that for the two feather star species examined, Capillaster multiradiatus and Clarkcomanthus mirabilis, both methods produce reasonably consistent results and that predation intensity is slightly lower on the latter perhaps because it responds to tactile stimulation by crawling deeper into its perch, whereas the former shows no response.

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)0.000
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 score0.008
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.175
GPT teacher head0.334
Teacher spread0.159 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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