Evidence‐based veterinary medicine in finfish aquaculture in Newfoundland\nand Labrador
Bibliographic record
Abstract
The Newfoundland and Labrador (NL) aquaculture industry is a major contributor of\nsalmon products that help meet protein needs for the world’s growing population. The industry\nin NL continues to grow and evolve and now includes emerging species in its production such as\nAtlantic cod. In a growing industry, with additional species being cultured, there are many\nopportunities to enhance evidence‐based veterinary medicine (EBVM). The multifaceted process\nof EBVM includes critically evaluating published literature related to a particular question.\nRandomized controlled trials (RCT) are considered the best source of information with respect\nto interventions, but these trials are challenging to implement in an aquaculture setting. The\naquaculture industry and their veterinarians need access to quality RCT in order to make sound,\nscientifically based health decisions. Therefore, it is the responsibility of the aquaculture\nindustry and their veterinarians to assist in building this knowledge base to further advance the\nindustry. The Newfoundland and Labrador Department of Fisheries and Aquaculture, together\nwith the Centre for Aquatic Health Sciences, have worked with the NL industry and their\nveterinarians to answer questions while contributing to the process of EBVM.\nThe specific objectives of this research program evolved over time, but were all\ngenerally focused on the need for information in support of EBVM. The research focused on two\nobjectives: evidence in support of trial execution and evidence from trials. The Passive\nIntegrated Transponder (PIT) tagging study in Atlantic cod was developed to determine tag\nplacement and evaluate adverse effects with such tag placement. This study was consistent with\nthe first objective. The second objective was addressed by developing three clinical trials\nrelevant to the NL industry at the time. These clinical trials were in response to questions\naround choosing treatment modalities for Eubothrium crassum, the option to use a\nnutraceutical during the smoltification stage of salmonid production and the use of a salmonid\ndip vaccine in Atlantic cod.\nThe tagging trial showed that there was no negative effect on survival and growth in\nAtlantic cod in the short term, thereby providing evidence to support the use of PIT tags in\nfuture studies.\nThe clinical trials showed (1) that treatment modalities adapted from terrestrial models\ndo not provide predictable results when information is simply transferred, (2) that\nnutraceuticals need to be critically evaluated with respect to their label claims, and (3) that\nvaccination of Atlantic cod may provide protection against pathogens not included in the\nvaccine due to non‐specific immunity.\nThe studies included in this thesis have contributed to the knowledge base used to\ninform aquaculture veterinarians who utilize EBVM. The results also highlighted techniques to\nobtain this information in an aquaculture setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".