An assessment of dead wood patterns and their relationships with biophysical characteristics in two landscapes with different disturbance histories in coastal Oregon, USA
Bibliographic record
Abstract
Understanding the relative importance of landscape history, topography, vegetation, and climate to dead wood patterns is important for assessing pattern–process relationships related to dead wood and associated biodiversity. We sampled dead wood at four topographic positions in two landscapes (1400–2100 km 2 ) that experienced different wildfire and salvage histories in coastal Oregon. Study objectives were to (i) determine whether and how the landscapes differed in dead wood amounts and characteristics and (ii) evaluate relationships between dead wood characteristics and potentially related biophysical variables associated with historical and current vegetation, topography, climate, soils, and ecoregion. Despite differences in history, the two landscapes differed little in total dead wood volume; however, they differed in dead wood volume by structural type, decay class, and source (legacy/nonlegacy). Dead wood varied by topographic position, and topography was of greatest importance compared with other factors. In this mountainous region, upper topographic positions may be source areas for dead wood and riparian areas and streams sinks for dead wood. Climate explained more variance in dead wood in the landscape that burned earlier and was not salvaged. Landscape-scale patterns of dead wood are evident in landscapes with different disturbance histories and despite finer-scale variation in topography, vegetation, and other biophysical attributes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".