Net efficacy of open ocean ballast water exchange on plankton communities
Bibliographic record
Abstract
We evaluated the efficacy of two ballast water exchange (BWE) methods during two transoceanic voyages of a bulk carrier in September 1999 and October 2000 between Rotterdam (The Netherlands) and Sept-Îles (Canada). The experimental design accounted for the uptake of new species during exchanges by considering only those taxa observed prior to BWE (initial taxa). To account for natural decreases due to mortality in the tanks, communities in exchanged ballast tanks were compared with those in control tanks, thus allowing the ‘net BWE efficacy’ of the procedures to be determined. The efficacy of the removal of organisms varied among BWE methods, plankton communities (microplankton and zooplankton) and taxonomic groups. BWE efficacy was greater for zooplankton (72–90%) than microplankton (49–80%). When the fairly high natural mortality observed in control tanks was considered in the calculation of BWE efficacy (net BWE efficacy), much lower efficacy was observed (microplankton: 29–40%; zooplankton: 23–54%). The 300% flow-through method (FT) is the most effective BWE method (net efficacy) for removing initial microplankton taxa (1999 and 2000), whereas the FT was either similarly (1999) or less than (2000) effective compared with the procedure normally carried out on board this vessel (NORM method) for the zooplankton community. However, BWE was more efficient in removing microplankton than zooplankton in 1999 while the opposite pattern occurred in 2000. The seasonal timing of voyages and the BWE site influenced the density and composition of species introduced to tanks during BWE.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".