Developing a simple yet rigorous approach for operational risk management for small vessels
Notice bibliographique
Résumé
Fishing is seen as one of the most dangerous occupations in the world, and the people affected by the accidents at sea are often among the poorest in the society as found by the International Labor Organization (ILO). About 95% of fishers worldwide are small scale fishers and it is estimated that as much as 40% of the global landings comes from small scale fisheries according to recent studies conducted by the Food and Agricultural Organization (FAO), in partnerships with Duke University and WorldFish. Some studies have in the past documented fishing accidents and spelt out various hazards and consequences relating to outcomes including injury, vessel damage and loss, and death. There is, however, limited information regarding national and global ranking of these hazards and consequences to help identify the patterns associated with the risk, and hence target training resources in the direction of most probable occurrences is difficult. It is therefore essential to study and assess the interactions among the influential risk factors and the management strategies that can be employed to mitigate their impacts and improve training. This research work seeks to study and develop a simple but rigorous operational risk modelling and management approach for small vessels that are used in fishing and transportation. A comprehensive probabilistic analysis was required to propose a simple applicable method to analyze risk causal factors of small fishing vessel operations. This was followed by the development of an operational risk model for small fishing vessels. The model was further analyzed with expert data along with secondary data from literature using a hybrid quantitative model for operational risk. In completing the research study, a case for an operational risk management approach for small fishing vessel is proposed using the cost per unit risk reduction (CURR) model to select a risk control option. Several small fishing vessel accidental events were attributed to operator error, vessel factors and environmental factors. Based on the findings of the research it is recommended that a combination of administrative and personal protective equipment control measures be adopted by the stakeholders.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| 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 ».