An analysis of the Modeling and Inventory Support Tool: Yield curves vary with Forest Ecosystem Classification
Bibliographic record
Abstract
Stand-level growth and yield models are essential to assessing sustainable levels of forest harvest; such assessments are supported in Ontario by the Modeling and Inventory Support Tool (MIST), which combines updated yield estimates and predicted successional trajectories to improve yield forecasts in Ontario. Currently, forest management planning and MIST stratifies the landbase by the Standard Forest Unit (SFU), but not ecosite as defined under the Forest Ecosystem Classification (FEC) system. Here we examined variation in MIST's input and output parameters (site index, top height, and basal area) for ecosites that fall within the definition of the PW1 SFU in Central Ontario (white-pine-dominated sites). Ecosites showed significant differences in site index values and top height, but not basal area, results indicating systematic differences in productivity among ecosites within the SFU. These results show that fine-scale variation in edaphic factors, as indicated by ecosite information, correspond to differences in stand productivity, and suggest the importance of a more harmonized approach between yield modeling, SFUs, and the FEC system in Ontario.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".