Study Of Morphometric Characters And Heavy Metals Detection In Knifefish (Notopterous Notopterous) Sampled From River Ravi, Pakistan
Bibliographic record
Abstract
Background: Notopterous notopterous belongs to catfishes and are 2nd main group of freshwater fishes. Objectives: To measure morphometric characters and heavy metals in fish from River Ravi. Methodology: Morphometric measurements was made by measurement tape and weight was calculated by digital balance. Heavy metals detection was also studied. Results: It was found that, fish weight was 105.85±26.620 g, length with tail 21.03±1.60 cm, length without tail 19.30±1.5 cm. The distance in eyes was 1.47±0.30 cm, in nostrils 0.7±0.09 cm. Mandibles length was 2.59±0.30 cm, and maxilla 3.21±0.19 cm, diameter of eye 0.97±0.08 cm. Length of fins was also compared. The number of rays of different fins was also seen. The relationship between length and weight was discovered to be non-linear, and development was allometric. The concentration of Cd was more in Liver, Cr was more in Liver, Cu was more in Liver and Pb was more in Gills then other organs of the fish. Conclusions: Fish had large head region with various kinds of fins including dorsal fins and anal fins. Iron, cadmium, Copper, Chromium and lead in different organs were also seen and nickel was high in all organs of body except gills. Lead accumulates in the gills
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".