An assessment of heavy metal levels in brackish water shrimps: Impact on sexes and the relationship between metal pollution index and Fulton's K condition indices
Bibliographic record
Abstract
We examined the temporal variation of heavy metals in different sexes of three brackish water shrimp species with emphasis on the relationship between Fulton's K condition indices and metal pollution index (MPI). Zn was the most abundant metal followed by Sn, though levels were below the admissible limits (irrespective of gender). Temporal variation of Zn and Pb was significant (p < 0.01). Drier weather conditions influenced Pb accumulation. Temporal variation of Sn was insignificant (p > 0.05), but temporal variation of Hg was significant only in Penaeus monodon (p < 0.0001). However, the relationship between temporal variation of the above-mentioned metals and gender was insignificant except in Penaeus semisulcatus for Pb (p < 0.02) and Sn (p < 0.04) despite no consistent higher bioaccumulation pattern by one particular sex. There were no significant negative correlations between the Fulton's K condition indices and MPI values in the different sexes. MPI values between the two sexes were insignificant. Any differences in the MPI values between the three species were insignificant. Only Penaeus semisulcatus exhibited significant differences (p < 0.022) in Fulton's K condition indices in relation to gender. Fulton's K condition indices are unreliable indicators of metal-induced stress levels in shrimps.
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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".