Cooperative Assessment of Natural Resource Injuries: Why Can It Work?
Bibliographic record
Abstract
ABSTRACT Around the world, typically, when a spill situation occurs, the main objective of the responsible party (RP) is to reduce the costs of the response as much as possible. At the other end, governments will want to protect the environment in the most efficient manner. These two positions will often clash resulting in stalled interventions to the disadvantage of both parties. Unfortunately, this situation is common and can lead to prosecution of the ship by governmental authorities. Examples of this behavior were recently observed in some Canadian cases where response was prolonged unnecessarily because of a lack of collaboration from RP. This paper will explore the benefits associated with a collaborative approach for the assessment of environmental damages between RP and governments. The authors will use a recent Canadian spill where such an approach was used to illustrate with concrete examples the benefits that where obtained.
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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".