Bibliographic record
Abstract
The pearl oyster industry is a growing multibillion dollar sector of mollusc aquaculture. Pearl farming occurs throughout Australasia, the Middle East and South America. Few species of molluscs possess the ability to produce pearls of gem quality. The South Sea pearl oyster is one of them. Pearl production in the wild is an unpredictable and uncontrolled event which human intervention, through pearl culture, has progressively overcome by improving culture practices. Farming mother-of-pearls shares commonalties with edible mollusc aquaculture. However, the endproduct, pearl production, is unique to this sector. In aquatic production, heal issues are of utmost importance; pearl production is based entirely upon health. The pearl itself is a product of the oyster’s immune defences as a response to soft-tissue irritation. Exploited stocks receive frequent handling stresses which often predispose farmed animals to infection and diseases. Therefore, the importance of health management for pearl oysters is paramount. Today, most disease problems are caused by opportunistic pathogens taking advantage of oysters weakened by the stress of handling, including pearl surgery and sub-optimal growing conditions. Except for the mass mortalities experienced in Japan, the pearl oyster industry have not yet faced the types of epizootics which has impacted mollusc culture elsewhere in the world. Development of the industry will, inevitably, lead to increased risk of disease introduction, spread or emergence. Against such an unwanted future, health management is the critical defence line. The objectives of this technical paper are to: (i) review pearl oyster mortalities and disease problems in order to help design programmes aimed at reducing the risks from diseases; and (ii) provide technical guidance to pearl oyster farmers and the industry on management of pearl oyster health so that sector development will be sustainable not only in providing huge employment to communities where pearl farms are located but also contributing to maintain environmental integrity. Pearl oyster farming can serve as environmental sentinels recognizing the fact that pearl oysters thrive only in pristine environment. This publication contains three parts. Part 1 consists of pearl oyster health — the current interest in it and an overview of the cultured marine pearl industry. Pan 2 on pearl oyster health management consists of seven sections, namely: (a) introduction; (b) general information on husbandry and handling, hatchery production, introductions and transfers; (c) disease diagnostic protocols dealing with field collections of samples, gross external examination, gross internal examination and laboratory protocols; (d) health zonation; (e) disease outbreak protocols; (f) national strategies on aquatic animal health; and (g) references. Certain countries in the pearl oyster producing regions have acquired a great deal of experience in health management of cultured species. Experiences from Australia, the Cook Islands, Japan, the French Polynesia, the Philippines, China, the Persian Gulf and the Red Sea are included in Part 3 which also contains a general review of pearl oyster mortalities and disease problems. [Book Synopsis]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.019 |
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".