Bibliographic record
Abstract
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
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".