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Record W2924923185 · doi:10.1101/584557

Population productivity of wedgefishes, guitarfishes, and banjo rays: inferring the potential for recovery

2019· preprint· en· W2924923185 on OpenAlexaff
Brooke M. D’Alberto, John K. Carlson, Sebastián A. Pardo, Colin A. Simpfendorfer

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsDalhousie University
FundersAustralian GovernmentNational Oceanic and Atmospheric AdministrationU.S. Department of Commerce
KeywordsExtinction (optical mineralogy)ProductivityPopulationBiologyValue (mathematics)Order (exchange)EcologyStatisticsDemographyMathematicsEconomicsSociology

Abstract

fetched live from OpenAlex

Abstract Recent evidence of widespread and rapid declines of wedgefishes, guitarfishes, and banjo ray populations (Order Rhinopristiformes), driven by a high demand for their fins in Asian markets and the quality of their flesh, raises concern about their risk of over-exploitation and extinction. Using life history theory and incorporating uncertainty into a modified Euler-Lotka model, maximum intrinsic rates of population increase ( r max ) were estimated for nine species from the four families of rhinopristiforms. Estimates of median r max varied from −0.04 to 0.60 year −1 among the nine species, but generally increased with increasing maximum size. In comparison to 115 other species of chondrichthyans for which r max values were available, the families Rhinidae and Glaucostegidae are relatively productive, while most species from Rhinobatidae and Trygonorrhinidae had relatively low r max values. If the demand for their high value products can be addressed, then population recovery for this species is likely possible but will vary depending on the species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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