Incorporation of colonization pressure into the propagule pressure‐based global ballast water standard
Bibliographic record
Abstract
Abstract Aim In 2024, cargo vessels must meet the International Maritime Organization's global ballast water discharge standards (IMO D‐2) that limit the concentration of living organisms. D‐2 focuses on reducing invasion risk by reducing ‘community propagule pressure (CPP)’, though it does not consider colonization pressure (CP). Location Global. Methods We modelled risk differences in IMO D‐2‐compliant discharges (10 ind. m−3) for communities that had inverse patterns of CP and species' individual propagule pressures (IPP). Secondly, we determined the effect on risk of varying CPP and CP. As part of this, we tested whether the IMO D‐2 standard for zooplankton‐sized organisms of <10 individuals m−3 was an optimal choice. Risk was defined as probability of at least one species invading using four risk–release models. Results Risk differed strongly at the D‐2 limit based on community composition. At low CPP (<25 ind. m−3), risk was strongly affected by CP for hyperbolic and linear risk–release models and weakly for exponential and logistic models, while CPP affected only the former two model types. Across a much wider range of CPP values, risk was affected by CP, CPP and by their interaction for all models. Main Conclusion The IMO D‐2 standard for zooplankton‐sized organisms requires very low CPP and even lower IPPs in mixed‐species releases, which will impede successful colonization. Species‐abundance theory predicts that discharges meeting the D‐2 standard (low CPP) will also have low CP. Much more empirical data are required to determine whether vessels can consistently reduce CP as it lowers CPP in order to meet these requirements.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".