Notice bibliographique
Résumé
The workforce needed to support future growth of aquacultureAquaculture will need to continue to grow to meet the growing needs of the global human population.All productive enterprises need the proper combination of inputs required for a successful business.These include natural resources such as land and water, capital to construct the necessary production facilities and associated buildings, and purchase the necessary equipment and operating inputs.Economists include management as an essential input for production because the decisions made by the manager are as essential to the success of the farm as are feed for the animals and electricity for aeration.Finally, all productive enterprises require an adequate quantity and quality of labor inputs to be successful.In aquaculture, much attention has been paid to the development of the farming practices, feeds, water quality, and other fundamental requirements for aquaculture production.The need for capital in sufficient quantities has been a frequent topic, particularly as related to investments to start up new aquaculture farming businesses.Until fairly recently, however, much less attention has been paid to understanding the quantity and nature of labor required for aquaculture to continue to grow.Educational programs have been developed in many countries for many years to provide an adequate workforce for aquaculture.These programs have been at various levels, often in vocational agricultural programs in high schools or two-year college programs as well as four-year and graduate university programs (European Commission, 2009;Curtotti, Hormis, & McGill, 2012;Jensen et al., 2015Jensen et al., , 2016;;Pita et al., 2015;Evans, 2019).Nevertheless, in a series of recent extensive surveys of aquaculture producers in the United States, a shortage of labor was cited as one of the top five problems confronting aquaculture producers (Engle, van Senten, & Fornshell, 2019;van Senten & Engle, 2017;van Senten, Engle, Hudson, & Conte, 2020;van Senten, Engle, & Smith, 2020).In Australia, an aging workforce in aquaculture and issues of recruitment and retention of an adequate workforce for aquaculture were described as growing problems (Curtotti et al., 2012).The remoteness of work areas and other competing employment opportunities with higher wages were found to compete with aquaculture and the broader seafood sector for both skilled and unskilled labor.Many young people prefer an office work environment and a more urban lifestyle with the associated amenities.In the EU as well, growing concerns related to a perceived mis-match between training programs and the needs of the labor market have been reported (Pita et al., 2015).The cost of labor has also emerged as an issue.Labor has been found to be a major cost of production in shellfish aquaculture in several countries and regions, including Taiwan (Huang, Lee, & Sun, 2013), France (Girard & Pérez
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,010 |
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 source (Gemma direct ou Codex distillé), 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 ».