Bibliographic record
Abstract
The objective of the current study was to analyze the performance of white shrimp fed using three feeding systems: manual, time feeders and sound feeders, in thirteen commercial semi-intensive farms in three areas of Guayas province (Ecuador) using 535 production lots during the period 2015~2017. The size of the ponds ranged between 3 and 23 hectares, with a depth of 1.2 m, in which the exchange of water was around 1 % a day. The initial weight of the shrimp was 0.04~0.29 g, and stocking density was around 10 shrimp per square meter. The three feeding systems used commercial diets with 35% protein content. The time feeder system gave a higher shrimp yield (1 631 kg/ha) than manual feeding (1 539 kg/ha) and the sound feeder system (1 483 kg/ha). The best results for survival were obtained with the manual system (61.7%) and the time feeder (62.9%) in comparison to sound feeder (57.0%). Current performance results with acoustic systems were lower than reported by other authors, probably because the number of feeders per hectare was low, reducing the accessibility of shrimp to feed, in fact daily feeding supplied was not improved as has occurred in other studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".