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Record W3019587788 · doi:10.1002/naaq.10153

Effect of Transport Method on Subsequent Survivorship and Gonad Yield/Quality in the Red Sea Urchin <i>Mesocentrotus franciscanus</i>

2020· article· en· W3019587788 on OpenAlexafffund
Emily M. Warren, Christopher M. Pearce

Bibliographic record

VenueNorth American Journal of Aquaculture · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of VictoriaFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsGonadSea urchinBiologyFisheryBroodstockSeawaterMusselAnimal scienceAquacultureZoologyFish <Actinopterygii>EcologyAnatomy

Abstract

fetched live from OpenAlex

Abstract Sea urchin gonad enhancement entails collecting adults with low gonad yields from the wild, placing them in land-based or sea-based captivity, and feeding them a prepared or natural diet to produce high quality/quantity gonads for marketing. Collection and transportation to the culture facility can be stressful to the urchins and cause subsequent mortalities and suboptimal gonad yield/quality. Minimizing this handling stress is critical to ensuring maximum survivorship and optimal gonad enhancement, yet very little research has examined this, with no information being available for the red sea urchin Mesocentrotus franciscanus. The present study examined survivorship, gonad yield, and gonad quality of red sea urchins 2 weeks after exposure to three 2-h transport methods: (1) milk crates placed in two plastic fish totes filled with ambient seawater (“wet crate”), (2) sealed Styrofoam boxes filled with ambient seawater (“wet box”), and (3) sealed Styrofoam boxes without seawater, but with wet burlap placed on top of the urchins (“dry box”). While no gonadal parameters significantly differed between transportation methods, the percentage of survivorship for both the wet boxes and wet crates was 100%, while that for the dry boxes was 58.3% after 2 weeks, indicating that wet transport should be preferred over dry transport for red sea urchins.

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 score0.130
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.248
Teacher spread0.231 · 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
Published2020
Admission routes2
Has abstractyes

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