Bacteria associated with the mucus layer of Merlangius merlangus (whiting) as biological tags to determine harvest location
Bibliographic record
Abstract
We describe a new technique to determine the harvest location of Merlangius merlangus (whiting) based on exploiting the natural bacterial populations associated with the mucus layer of fish and surrounding seawater as biological tags. Bacterial community profiles from the outer and mouth mucus and the surrounding seawater were characterized by terminal restriction fragment length polymorphism (T-RFLP), and fish harvest location was predicted based on T-RFLP profile comparisons between fish and water and between individual fish. Fish harvest location was not resolved reliably based on analysis of the shared seawater and outer-mucus markers. However, comparisons based on shared seawater and mouth-mucus markers placed fish within 1.92 (± standard error (SE) of 0.31) stations (p < 0.002), equivalent to 153.0 km, of their known harvest location. Fish placement resolution was further increased when fish-to-fish bacterial assemblage comparisons were made. Based on outer-mucus, mouth-mucus, and combined outer- and mouth-mucus analysis, fish were placed within 1.00 (±SE 0.27), 1.55 (±SE 0.28), and 0.71 (±SE 0.24) (p < 0.04) stations, equivalent to 79.7, 123.5, and 57.3 km, respectively, of their known harvest location.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".