Tidal periodicity of molting in giant mud crab, Scylla serrata (forskål, 1775)
Bibliographic record
Abstract
Molting is the most crucial phase in the life cycle of mangrove crabs, leading to mortality in various culture systems. As a result of this exigency, this study was conducted to offer a cue in molting using the tidal cycle as one of the visible events in a natural context. Monitoring was carried out at every 1-hour interval (24 hours) day and night for 57 days to check for molting while taking into account key environmental elements such as tidal cycles, water current speed, and flow rate. The results showed that 70.6% of molting happened during high tide, with 93.8% of it occurring at night and 6.3% during the day. By contrast, only 29.4% occurred during low tide, showing a significant difference (T-test = 0.011, p < 0.05) between mean molts. Hence, the highest molting rate (88.2%) was observed between tidal episodes between the neap and middle cycles, with the middle tide (55.9%) occurring at 12.4±0.9 m/s, neap tide (32.4%) occurring at 16.8±2.0 m/s, and spring tide (11.8%) occurring at 9.5±1.2 m/s. Molting occurs at high tide because crabs' rheotactic behavioral responses to incoming new water help the crab's body fill with air and water throughout the molting process. Mangrove crabs employ their tidal periodicity of molting as a defense strategy against potential predators and mortality, and it can be used as a molting indicator in many culture systems.
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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 | 0.001 | 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".