Multi-cohort stand structure in boreal forests of northeastern Ontario: Relationships with forest age, disturbance history, and deadwood features
Bibliographic record
Abstract
Multi-cohort forest management (MFM) is a natural disturbance emulation strategy for boreal forests that recommends a diversification of silvicultural techniques to emulate three broad successive phases of post-fire development, termed “cohort classes”. Here, for boreal mixedwood (n = 308) and black spruce (n = 108) stands of northeastern Ontario, we: 1) present a multivariate approach to classify the three cohort classes based on a broad set of stand structural variables related to live-tree diameters, densities, and measures of canopy stratification and 2) investigate variation in stand age, mode of stand origin (including horse- and mechanically logged and natural-origin stands), and deadwood features among the cohort classes. In both forest types, average stem diameter distributions in cohort class 1 were normally distributed, those in class 2 showed broader normal distributions, and those in class 3 showed inverse-J distributions. Mean stand age increased with cohort class, and was positively correlated with cohort class in both forest types. Overall, variation in age and deadwood features as a function of cohort class in both forest types provided strong support for developmental aspects of our cohort classifications. Previously logged stands were primarily associated with lower cohort classes, whereas natural-origin stands were strongly associated with complex cohort class 3 stand structures, especially in mixedwoods. As it is primarily the silvicultural manipulation of stand structure that has been proposed to emulate age-related multi-cohort development, our structural cohort classification approach is particularly relevant to the application of MFM 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".