Characterizing forest structural types and shelterwood dynamics from Lorenz-based indicators predicted by airborne laser scanning
Bibliographic record
Abstract
In this study, Lorenz curve descriptors of tree diameter inequality were used to characterize the dynamics of forest development in a shelterwood-managed Pinus sylvestris (L.) dominated area. The purpose was to stratify the forest area into forest structural types (FST) from airborne laser scanning (ALS)-based wall-to-wall predictions of the chosen indicators: Gini coefficient (GC) and Lorenz asymmetry (LA). A clear boundary at GC = 0.5 was found, which separated even-sized (below) and uneven-sized (above) areas. Furthermore, a need for including LA in the characterization of the uneven-sized areas was detected, to distinguish bimodal from reverse J-shaped stands. Beta regression was used for the ALS predictions, yielding RMSEs of 19.67% for GC and 11.01% for LA. Based on our results, we concluded that forest disturbance decreases GC, whereas seed regeneration increases GC and, therefore, gap dynamics are characterized by shifts between either side of the GC = 0.5 threshold. In even-sized stands, GC decreases toward maturity owing to self-thinning occurring at the stem exclusion stage. In uneven-sized stands, the skewness of the Lorenz curve indicates understory development, as ingrowth decreases LA. The possible applications of the resulting FST map are discussed; for instance, in identifying areas needing silvicultural treatments or evaluating forest recovery from disturbances.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".