Modeling early forest succession following clear-cutting in western Oregon
Bibliographic record
Abstract
In the Pacific Northwest, the process of conifer development after stand-replacing disturbance has important implications for many forest processes (e.g., carbon storage, nutrient cycling, and biodiversity). This paper examines conifer development in the Coast Range Province and Western Cascades Province of Oregon using repeat interpretation of historic aerial photographs from 1959 to 1997 to examine the canopy cover change of different life forms: shrubs, hardwood trees, and conifer trees. Ninety-four stands from the Western Cascades Province and 59 stands from the Coast Range Province were photointerpreted in roughly 5-year intervals. A ChapmanRichards growth function was used to model conifer cover development for all sample stands. Based on the photo data and the ChapmanRichards function, these stands were classified into one of seven early forest successional trajectories defined by the vegetation physiognomy. Succession in the Coast Range Province and Western Cascades Province were compared using parameters derived from the ChapmanRichards growth function. Our results echo previous studies in that rates and densities of conifer regeneration varied markedly among sites; however, our results also indicate that early forest succession differs in the two study regions in terms of both trajectories and rates. Conifer regeneration in the Western Cascades Province tends to have longer delays in establishing and slower rates compared with the Coast Range Province.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".