Evaluating Efficacy of an Environmental Policy to Prevent Biological Invasions
Bibliographic record
Abstract
Enactment of any environmental policy should be followed by an evaluation of its efficacy to ensure optimal utilization of limited resources, yet measuring the success of these policies can be a challenging task owing to a dearth of data and confounding factors. We examine the efficacy of ballast water policies enacted to prevent biological invasions in the Laurentian Great Lakes. We utilize four criteria to assess the efficacy of this environmental regulation: (1) Is the prescribed management action demonstrably effective? (2) Is the management action effective under operational conditions? (3) Can compliance be achieved on a broad scale? (4) Are desired changes observed in the environment? The four lines of evidence resulting from this analysis indicate that the Great Lakes ballast water management program provides robust, but not complete, protection against ship-mediated biological invasions. Our analysis also indicates that corresponding inspection and enforcement efforts should be undertaken to ensure that environmental policies translate into increased environmental protection. Similar programs could be implemented immediately around the world to protect the biodiversity of the many freshwater ecosystems which receive ballast water discharges by international vessels. This general framework can be extended to evaluate efficacy of other environmental policies.
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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 | 0.060 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".