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Record W2093535347 · doi:10.7901/2169-3358-2003-1-1151

Environmental Damage Assessment - Canadian Style

2003· article· en· W2093535347 on OpenAlexaffabout
Roger Percy, Sinclair Dewis, P. Hennigar

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsDamagesProcess (computing)Environmental restorationCompensation (psychology)Risk analysis (engineering)Environmental planningEnforcementEnvironmental impact assessmentEnvironmental resource managementBusinessComputer scienceEnvironmental sciencePolitical sciencePsychologyLaw

Abstract

fetched live from OpenAlex

ABSTRACT Damage Assessment involves evaluating and monetizing damages for compensation purposes. This process is meant to compliment enforcement activities by providing a framework for securing funds for restorative and prevention measures. In recognition of the growing need to address the issue of restoration of and compensation for environmental damages incurred as a result of pollution incidents Environment Canada has undertaken an initiative to develop and implement a national approach to environmental damage assessment and restoration. This paper will describe the steps taken by Canada to establish a practical framework for an environmental damage assessment/restoration process. It will highlight steps taken to reach consensus and to educate stakeholders, identify available legal instruments, describe development of guidelines/protocols for scientific assessment as well as the mechanism for decision making.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.013
Science and technology studies0.0040.001
Scholarly communication0.0050.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0330.004

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.

Opus teacher head0.013
GPT teacher head0.260
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes2
Has abstractyes

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