Evaluation of Three External Marking Methods of Farmed Atlantic Salmon for the Future Use of Differentiating it From Wild Atlantic Salmon
Bibliographic record
Abstract
We evaluated different external marking methods for farmed salmon to differentiate it from wild salmon without any special tools.Three marking methods were tested: 1) Adipose fin (AF) removal, 2) Freeze branding (FB) and, 3) Visible Implant Elastomer (VIE).Location of the marking method on the fish, combination of marking methods and degree of AF removal were tested in three experiments.Atlantic salmon parr weighing 20 g were marked either with individual marks or in combination of two.Further all the fish were also PIT tagged.They were kept in freshwater tanks for 4 months and later after smoltification, smolts were transferred to sea cages and kept for another 4 months.At the end of four (freshwater phase) and ten (sea cages) months, growth, survival and mark retention were recorded.All these methods had no significant effects on growth and survival compared to the control (no mark but only PIT tagged).Our results showed that of these methods, only complete removal of the adipose fin met the requirements for mark retention and was the cheapest and easiest method to automate.However, a larger commercial scale long-term testing of the AF clipping is required prior to implementing it.Further development of an automated fin clipping in combination with vaccination and an open discussion with consumers, buyers, and environmental groups are also warranted.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 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".