Growth, Reproductive Performances, and Brood Quality of Long Snout Seahorse, <i>Hippocampus guttulatus</i>, Fed Enriched Shrimp Diets
Bibliographic record
Abstract
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 < 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 < 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".