Natural and logging disturbances in the temperate rain forests of the Central Coast, British Columbia
Bibliographic record
Abstract
Natural disturbances frame the spatial and temporal processes of ecosystems and are the foundation for ecosystem-based management. In the coastal temperate rain forests of British Columbia, landscape patterns of natural disturbances and their contrasts with logging are not well documented. Stand-replacing disturbances over the past 140 years were investigated for the Central Coast (1.5 million ha) at regional and local scales using a combination of aerial photograph interpretation and forest management GIS databases. At the regional scale, stand-replacing natural disturbances affected 3.1% of the forested area. The extent of natural disturbances was not strongly affected by the scale of analysis. In contrast, spatial pattern and scale were essential for discerning the full impact of logging. At the regional scale, logging affected 5.4% of the forested area. Within watersheds, however, logging occurred primarily in valley bottoms (81% ± 4%) with 59% ± 10% of valley bottom areas logged, 10 times the area of natural disturbances. Watershed size strongly affected riparian zones, with active floodplains comprising 53% ± 5% of valley bottom area in large (>20 000 ha) watersheds. In physiographically diverse landscapes, geomorphic features (such as watersheds, valley bottoms, and fluvial landforms) are crucial for determining disturbance processes and effects of logging at ecologically relevant scales.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".