Data from: Possible ballast water transfer of lionfish to the eastern Pacific Ocean
Bibliographic record
Abstract
The Indo-Pacific Red Lionfish was first reported off the Florida coast in 1985, following which it has spread across much of the SE USA, Gulf of Mexico, and Caribbean Sea. Lionfish negatively impact fish and invertebrate assemblages and abundances, thus further spread is cause for concern. To date, the fish has not been reported on the Pacific coast of North or Central America. Here we examine the possibility of ballast water transfer of lionfish from colonized areas in the Atlantic Ocean to USA ports on the Pacific coast. Over an eight-year period, we documented 27 commercial vessel-trips in which ballast water was loaded in colonized sites and later discharged untreated into Pacific coast ports in the USA. California had the highest number of discharges including San Francisco Bay and Los Angeles-Long Beach. A species distribution model suggests that the probability of lionfish establishment is low for the western USA, Colombia and Panama, low to medium for Costa Rica, Nicaragua, El Salvador and Guatemala, medium to high for mainland Ecuador, and very high for western Mexico, Peru and the Galapagos Islands. Given the species’ intolerance of freshwater conditions, we propose that ballast water exchange be conducted in Gatún Lake, Panama for western-bound vessels carrying ‘risky’ ballast water to prevent invasion of the eastern Pacific Ocean.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".