Risks of Introductions of Marine Fishes: Reply to Briggs
Bibliographic record
Abstract
Abstract This is a rebuttal to a publication by John C. Briggs in the April 2008 issue of Fisheries in which he suggested introducing fishes and invertebrates from the North Pacific into the North Atlantic to increase diversity toward improving fisheries in the latter. We argue otherwise for reasons that Briggs downplayed or never considered. Using examples of introductions within the Pacific and the Atlantic, and movements of species from the Pacific to the Atlantic, we provide a record of failures and damage or dangers to native species from the few introductions that became successful. We argue that a lack of diversity of fishes and invertebrates in the North Atlantic versus that of the North Pacific is not the problem to be corrected by introductions as Briggs suggested. A record of overfishing and management policies is the problem in the North Atlantic. Introductions from the North Pacific to the North Atlantic are not worth the costs or the environmental risks involved.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 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".