Comparisons of spatial patterns between windthrow and logging at two spatial scales
Bibliographic record
Abstract
Windthrow is a dominant natural disturbance in the boreal forest of eastern Canada. To provide the range of variability of a natural disturbance, its spatial distribution and patch metrics at stand and landscape scales have to be considered, together with the characterization of its severity. Our study characterized both partial windthrow (PW) and total windthrow (TW) spatial distributions at the landscape scale and patchiness within affected stands (stand scale). Landscape scale corresponded to three areas of about 5000 ha. Stand scale was the finest scale of analysis and corresponded to each affected stand within landscapes. In addition, windthrow spatial characteristics were compared with spatial characteristics of harvested areas (CUT). At the landscape scale, our results showed that TW stands were more isolated than PW stands and that mean shape complexity of disturbed stands was low, regardless of whether the disturbance was a windthrow or a harvested area. At the affected stand scale, residual trees covered a significantly higher proportion of PW stands than TW stands and CUT. CUT and TW did not share many spatial characteristics at the stand scale. CUT had a significantly higher proportion of complete canopy openness than TW. Our results showed that PW are spatially heterogeneous at both landscape and stand scales. In an ecosystem management context, i.e., a management that reduces the discrepancy between natural and managed forests, our results showed that forest managers could practice a variety of harvesting methods of different intensities.
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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 | 0.000 | 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) | 0.001 | 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".