MétaCan
Menu
Back to cohort

Growth, Reproductive Performances, and Brood Quality of Long Snout Seahorse, <i>Hippocampus guttulatus</i>, Fed Enriched Shrimp Diets

2012· article· en· W1970793059 on OpenAlexafffund
Jorge Palma, José Pedro Andrade, Dominique Bureau

Bibliographic record

VenueJournal of the World Aquaculture Society · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsShrimpBiologySeahorseBroodFisheryAnimal scienceEcology

Abstract

fetched live from OpenAlex

This investigation examined the effect of using enriched shrimp (Atlantic ditch shrimp, Palaemonetes varians ) diets on growth of long snout seahorse, Hippocampus guttulatus , and its effect on the reproduction rate and brood quality. Three diets were (1) natural wild‐caught shrimp (natural shrimp diet‐control diet), (2) wild‐caught shrimp fed an artificial feed for 10 d (enriched shrimp diet), and (3) wild‐caught shrimp fed one large meal of artificial diet and immediately frozen (ingested artificial feed shrimp diet). These diets were fed to seahorses during a 12‐wk growth trial. At the end, significant differences on the final wet weight were found between seahorses fed the three different treatments ( P &lt; 0.009). Seahorses fed ingested artificial feed shrimp diet had more broods (9), generated more juveniles per brood (299 ± 87), and significantly bigger juveniles (12.4 ± 1 mm) than seahorses fed natural shrimp diet and enriched shrimp diet. Significant differences in the morphometry of juveniles hatched from parents fed the three different dietary treatments (Wilk's λ = 0.2, F (6,460) = 47.41, P &lt; 0.0001) were also found. Results indicate that the combined use of a natural diet (shrimp) and an artificial diet benefit growth and feed utilization by seahorses and have a direct impact on the reproductive rate and brood quality of H . guttulatus .

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 score0.220
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.259
Teacher spread0.234 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the World Aquaculture SocietySame topicAquatic life and conservationFrench-language works237,207