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Record W4408700599 · doi:10.1155/are/7950801

Utilization of Algal Turf Scrubber Biomass in Sea Urchin ( <i>Lytechinus variegatus</i> ) Diets

2025· article· en· W4408700599 on OpenAlexaff
Magnus Krever, Kosta Tzanis, Destiny Sauls, R Stephenson Gerald, Henna Gavem, Anthony Siccardi

Bibliographic record

VenueAquaculture Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsCanada Research Chairs
FundersGeorgia Institute of TechnologyGeorgia Southern UniversityNational Science Foundation
KeywordsLytechinus variegatusBiologyBiomass (ecology)Sea urchinBotanyScrubberFisheryAgronomyEcology

Abstract

fetched live from OpenAlex

This study evaluated the growth indices in sea urchins fed varying levels of algal turf scrubber (ATS) biomass for the partial replacement of dietary menhaden fishmeal. Juvenile Lytechinus variegatus were fed four formulated diets with differentiating levels of ATS biomass (10%, 10%, 20%, and 50%). Biomass was harvested from two ATS systems, one receiving treated wastewater effluent and the other incorporated into a hydroponics system. A 12‐week growth trial was conducted, and each sea urchin ( n = 100) was weighed and fed daily. Diets were formulated using Agri‐Data Systems Pro 5 (Version 2.41, Agri‐Data Systems). Four isonitrogenous and isocaloric diets were prepared. At the end of the growth trial, no significant differences were found in final weight, final diameter, and survival across all diets. All treatments performed well based on condition level and higher levels of ATS biomass did not indicate reduced growth or dietary utilization. Final weights of dry test, and dry gut, were not significantly different between treatments, except for dry gonad compared with menhaden fish meal. These findings indicate that in sea urchin diets, partial replacement of fish meal with ATS biomass is well tolerated.

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: none
Teacher disagreement score0.508
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.044
GPT teacher head0.363
Teacher spread0.320 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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