Stake net Catch analysis of Ashtamudi Lake
Bibliographic record
Abstract
A general account on the stake net catch of Ashtamudi Lake during 2009 November- 2010 October is given. Estuaries and back waters are the back bone of marine fishery resources as they serve as the nursery for many of the penaeid prawns and fishes. Stake nets are widely used in the back waters, estuaries and coastal areas and it plays an important role in the commercial exploitation of prawns and fishes. The present study was aimed to analyze the stake net catches, which operated along the Ashtamudi lake. Prawns were contributed over 60% of the total catch. Among them, Penaeid prawns was the major contributor (93%). Fishes contributed only 32% of the total catch followed by mollusks (1%). As prawns become the major contributor of this gear, this net can be considered, a typical Prawn Fishing gear.
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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".