Using digital cover photography to track the canopy recovery process following a typhoon disturbance in a cool–temperate deciduous forest
Bibliographic record
Abstract
Climate extremes impact the function, structure, and composition of terrestrial ecosystems, while ecosystem responses to climate extremes differ with variations in frequency, intensity, and timing of the extreme event. We examined the canopy recovery processes following a typhoon disturbance in a cool–temperate deciduous forest in northern Japan based on 6-year data of canopy coverage imagery using a digital cover photography (DCP) approach that estimates canopy metrics relevant to leaf and woody masses, spatial dynamics, or arrangement of foliage elements. The DCP-derived imagery detected increases in leaf area index and foliage cover within 2∼3 years after the typhoon (i.e., recovery to the pre-typhoon state). Meanwhile, the recovery in leaf area and foliage cover observed after 6 years resulted from a spatial re-arrangement of the foliage elements (i.e., small within-crown gap fraction, foliage clumping) with increasing canopy space availability. Thus, the recovery of the spatial arrangement of foliage elements after the typhoon to the pre-typhoon state takes longer than the recovery of the leaf and woody masses. This study provides an important ecological implication in terms of possible resilience adaptation for ecosystem function and structure following extreme climate events.
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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 | 0.000 |
| 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) | 0.000 | 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".