Regional environmental assessment of forest management : experience in Ontario and Minnesota
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it