Forest structure following tornado damage and salvage logging in northern Maine, USA
Bibliographic record
Abstract
Understanding forest structural changes resulting from postdisturbance management practices such as salvage logging is critical for predicting forest recovery and developing appropriate management strategies. In 2013, a tornado and subsequent salvage operations in northern Maine, USA, created three conditions (i.e., treatments) with contrasting forest structure: blowdown, blowdown + salvage, and control (undisturbed). We sampled forest structure in five stands representing each of these three treatments. Our results document obvious and predictable changes to forest structure caused by the blowdown and salvage operations; however, they also include unexpected findings: downed coarse woody debris volume remained quite high in the salvaged areas, although its vertical distribution was markedly reduced; salvage operations did not reduce fine woody debris volume; and the salvage operation itself reduced the abundance of upturned root masses. Our study contributes to a growing body of literature highlighting the fact that outcomes of salvage operations vary considerably from situation to situation. Nevertheless, they suggest that salvage logging has important implications for residual stand structure and regeneration potential and that these implications should be considered carefully when weighing postdisturbance management options.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".