Is <i>Intensive Forest Management</i> a misnomer? An Ontario-based discussion of terminology and an alternative approac
Bibliographic record
Abstract
The term forest management refers to the science and business of operating a forest property, which, on Crown lands in Ontario, is typically a forest management unit. Silviculture is a component of forest management that refers to the suite of stand-level activities used to control stand composition and growth. Intensive forest management (IFM) is a concept that has been discussed and considered in Ontario for at least 30 years. Originally, it referred to an intensively managed forest in which most stands are subject to relatively intensive silvicultural practices. Over time, both professional foresters and stakeholders began using the term IFM as if it were synonymous with intensive silviculture. As a result, IFM has been inappropriately used to reference stand-level activities in several published definitions and key policy documents, creating confusion among the science community, professionals, and the public. This confusion has made it difficult to implement aspects of the 1999 Ontario Forest Accord, which calls for the use of IFM (meaning intensive silviculture) to increase forest growth and productivity in some areas to offset the withdrawal of lands for parks and protected areas. We call on forest managers to refer to the term IFM correctly and to portray forest management to stakeholders as consisting of a portfolio of natural and/or anthropogenic disturbance regimes. With this approach, forest managers could more meaningfully define the intensity of forest management and silviculture on their landbase.Key words: forest policy, land use planning, intensive silviculture, portfolio concept of forest management, triad principle of land-use zoning, Forest Research Partnership, NEBIE Plot Network
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".