MétaCan
Menu
Back to cohort
Record W4287146928 · doi:10.1017/pab.2022.22

Reassessing growth and mortality estimates for the Ordovician trilobite <i>Triarthrus eatoni</i>

2022· article· en· W4287146928 on OpenAlexaff
Daniel Pauly, James D. Holmes

Bibliographic record

VenuePaleobiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTrilobiteExtant taxonOrdovicianMarine invertebratesPaleontologyBiologyInvertebrateEcologyPopulationGeologyEvolutionary biologyDemography

Abstract

fetched live from OpenAlex

Abstract A length–frequency sample ( n = 295) from a fossil population of the Ordovician trilobite Triarthrus eatoni Hall, 1838, assembled and analyzed by J. L. Cisne in 1973 is here reexamined using methods of length–frequency analysis commonly used in fishery science and marine biology. Theoretical considerations and the empirical data at hand suggest that the growth of T. eatoni was not “linear,” but asymptotic, as is the growth of most Recent marine invertebrates. The parameters of the von Bertalanffy growth function ( L ∞ = 41 mm, K = 0.29 yr −1 ) suggest that T. eatoni , which apparently lived in a challenging environment, grew somewhat more slowly than the extant marine isopod Ceratoserolis trilobitoides (Eights, 1833), used here as Recent analogue to T. eatoni. This trilobite probably lived up to 10 years, rather than the suggested 4 years, and its mortality rate was 15%–20% per year rather than 30%–40% per year. These represent the first estimates of trilobite absolute growth characteristics using methods known to accurately model growth in extant water-breathing ectotherms. These provide a baseline for trilobite growth that can be used to make inferences about growth in other species. The approach used here may also be applied to other trilobites for which suitable length–frequency data exist.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.286
Teacher spread0.261 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuePaleobiologySame topicMarine and fisheries researchFrench-language works237,207