Regional environmental assessment of forest management : experience in Ontario and Minnesota
Bibliographic record
Abstract
Environmental assessment (EA) was originally conceived as a process applying to \ndiscrete projects such as power dams and timber harvest plans, but increasingly it is \nbeing applied to programs and policies for large areas. Such is the case for forest \nmanagement, where EA is finding application to regional management strategies. The \naim in this study was to investigate and analyze the quality of two regional EAs of forest \nmanagement; (a) the Ontario Class EA for Timber Management on Crown Lands in \nOntario; and (b) the Minnesota Generic Environmental Impact Statement (GEIS) on \nTimber Harvesting and Associated Forest Management Activities. The Ontario EA was \na difficult hearing-dominated venture where experts brought testimony before a quasi-judicial \ntribunal. The Minnesota EA centred upon quantitative impact analyses \nundertaken by inter-disciplinary study teams and documented in concise reports. Both \nthese EAs looked at forest management issues across huge areas, and both were \ncompleted in 1994. \nA broad cross-section of criteria derived from EA literature was used to judge the \nquality of the EAs, including factors pertaining to elements of process, technical and \nscientific requirements, and outcomes. I applied the criteria in describing and evaluating \nthe two EAs and found them generally to contrast strongly with each other. The paper \nsummarizes the strengths and weaknesses of the two EAs so that similar endeavours in \nthe future can be designed to avoid some of the pitfalls encountered in the preparation \nof the Minnesota and Ontario regional environmental assessments.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".