Redistribution of heterotrophic prokaryotes through ballast water: A case study from the west coast of Canada
Bibliographic record
Abstract
Oceangoing ships contribute to the introduction of invasive, benign and pathogenic bacteria via ballast water discharge. Here we report the bacterial abundance and cell size in ballast and receiving port waters in Vancouver, British Columbia (Canada) ports during 2007 and 2008. Bacterial abundance in port water (7.5×10 8 to 3.4×10 9 cells L -1 ) was significantly (P < 0.001) higher than those in ballast water (2.5×10 8 to 2.1×10 9 cells L -1 ) and was higher in unexchanged than ballast water that was exchanged at-sea. There was no significant difference in bacterial abundances between 2007 and 2008 for each sample type. Bacterial cell volume showed a different pattern, with no significant difference among sample types and a two-fold larger average cell volume during 2007 than 2008. Bacterial abundance and cell volume in ballast water were not correlated with ballast water age, end-of-voyage temperature, salinity or pH. The absence of predictive relationships between measured physiochemical and bacterial variables in ballast water highlights the difficulty of predicting bacterial abundance or cell volume from the physiochemical factors alone. Future studies should focus on the bacterial community structure in ballast and port waters, the fate of bacteria in the new environment, and regional susceptibility to invasion by the introduced bacteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".