Preliminary experiments on application of participatory GIS in trawl \nfisheries of Karnataka and its prospects in marine fisheries resource \nconservation and management
Notice bibliographique
Résumé
Geographic Information System (GIS) has become a part of our day today life in empowering institutions to formulate \nacceptable solutions in societal issues. More recently, public participatory GIS (PPGIS) and participatory GIS (PGIS) were \nviewed as more efficient tools in solving social and resource conservation issues, which empower communities those who \nare often ignored in traditional GIS practices. In fisheries, PGIS concept was first reported from Canada and on these lines \npioneering efforts of involving concept of PGIS in fisheries is being attempted in Karnataka, where the geospatial data on \nfishing, catch and samples of fish caught by commercial fishing vessels were shared with the research organization and the \ndata and samples thus shared were processed by fishery and GIS experts to come out with various tools for fishery management \nand resource conservation of the region. The study showed that the trawlers from Mangalore carried out trawling operations \nfrom sea off Calicut in the south (75 oE, 11 oN) to off Ratnagiri in the north (73.5 oE, 17 oN). Their depth of operation was \nbetween 5 m and 167 m, which signify the importance of revalidation of state–wise policies in introduction of mechanized \nvessels based on the landing in the respective states. The study showed that during the period, 237 species / groups of marine \nfauna were discarded of which many were juveniles of commercial species and rest were of adult size fishes of low or no \nmarket value. Spatio-temportal distribution and abundance of commercially lesser known species, which was not reported \nearlier from the coast,which have high trophic importance like small crabs, Charybdis hoplites and shrimp species like \nMetapenaeus andamanensis were brought out as the results of the study. Study also provided information on a unique \necosystem off Karnataka coast and with reef species and there is an immediate need for conserving this ecosystem. Based on \nthe results of distribution and abundance of marine resources, spatial and seasonal restrictions on fishing efforts can be \nadvocated in areas and seasons during which high incidence of bycatch of juveniles and non-commercial biota is being \ncaught. This will help in sustaining marine fisheries from Malabar and Konkan coasts.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».