Preliminary experiments on application of participatory GIS in trawlfisheries of Karnataka and its prospects in marine fisheries resourceconservation and management
Bibliographic record
Abstract
Geographic Information System (GIS) has become a part of our day today life in empowering institutions to formulate acceptable solutions in societal issues. More recently, public participatory GIS (PPGIS) and participatory GIS (PGIS) are viewed as more efficient tools in solving social and resource conservation issues, which empower communities those who are often ignored in traditional GIS practices. In fisheries, PGIS concept was first reported from Canada and on these lines pioneering efforts of involving concept of PGIS in fisheries is being attempted in Karnataka, where the geospatial data on fishing, catch and samples of fish caught by commercial fishing vessels were shared with the research organization and the data and samples thus shared were processed by fishery and GIS experts to come out with various tools for fishery management and resource conservation of the region. The study showed that the trawlers from Mangalore carried out trawling operations from sea off Calicut in the south (75 o E, 11 o N) to off Ratnagiri in the north (73.5 o
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".