Uncertainty in expert knowledge of forest succession: A case study from boreal Ontario
Bibliographic record
Abstract
Expert knowledge of forest succession is used widely in forest management planning, but its level of uncertainty is unknown. Using boreal Ontario as an example, we examined the level of uncertainty in expert knowledge of forest succession and explored possible sources of this uncertainty. Overall, the level of uncertainty associated with expert knowledge was high for all aspects of forest succession, except for post-fire species establishment. Higher levels of uncertainty were associated with knowledge of forest succession for mixed forest types and moderate site conditions, as opposed to coniferous or non-coniferous forest types and extreme dry/wet or poor/rich sites. We hypothesize that uncertainty in expert knowledge is highest when vegetation dynamics are highly stochastic as with complex species assemblages, environmental controls on succession are weak, and effects of disturbances are less drastic. Awareness about the degree of uncertainty in expert knowledge of forest succession could be incorporated into forest management decision processes. It could also help researchers to identify critical knowledge gaps to guide further studies. Key words: uncertainty assessment, post-fire establishment, natural succession, knowledge elicitation
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".