Effect of Mechanical Vibrations During Transport Operations of Nilo Tilapia (Oreochromis niloticus)
Bibliographic record
Abstract
Oscillatory movements present in the transport of live fish may compromise the physiological stability and the future performance of the animals. Therefore, the objective of this research was to evaluate the effect of mechanical vibrations in the transport of Nile tilapia through vibration levels and shocks occurred in transport boxes previously installed in a truck. The research was carried out in a fish farming integrating company in the state of Ceará, Brazil, with the monitoring of 5 live fish loads. The transport truck used was of the open type, with capacity for five boxes of fiberglass with a useful volume of 2400 L, density of 236 kg m-3. The data were recorded through five dataloggers, to monitor the vibration level (m s-2) and the occurrence and amplitude of mechanical shocks on the roads. Hematological, metabolic and ionic responses of fish were evaluated as well as visual observations of physical injuries. The most intense shocks occurred with the truck between 60 and 80 km h-1, with vibrations 1.151 m s-2 in the transport box, as well as in the water 0.489 m s-2. Larger vibration levels occurred on the asphalt road, with an average value of 1.13 m s-2, while on the dirt road they registered an average of 0.57 m s-2. Vibratory and mechanical stimuli presented secondary responses to blood level stress with alterations in glycemia, hematocrit, hemoglobin and magnesium ions. Physical lesions with 34% severe and 21% moderate, showed an uncomfortable environmental condition to fish.
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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".