Anatomy of a decision: Potential regulatory outcomes from changes to chemistry protocols in the Canadian Disposal at Sea Program
Bibliographic record
Abstract
Environment Canada currently assesses dredged material proposed for disposal at sea using a two-tiered assessment framework. Tier 1 determines sediment geophysical properties and concentrations of four regulated chemical constituents (Cd, Hg, PAH and PCB), and "other chemicals of interest" based on lower action levels; this is followed by biological assessment. EC is pursuing a "data mining" approach to evaluate potential refinements by compiling sediment chemistry and toxicity datasets, and subjecting them to a series of decision protocols. This paper reports on database development and initial use, and recommends potential changes to Tier 1 chemical protocols and further work to address other aspects of the framework. Major findings include the poor performance of Hg and Cd as sentinels for other metals, the significance of the list of analytes (vs. the specific SQGs used) in decisions, and the potential for chemical upper action levels to save the expense of unnecessary toxicity testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".