Assessing factors that influence spatial variations in duff moisture
Bibliographic record
Abstract
Abstract Patterns and spatial variations in the moisture of the decomposing organic matter on the forest floor (the duff) of a montane forest were analysed in an effort to determine the primary factors shaping these patterns. Above and below canopy meteorological conditions were monitored to determine the influence of canopy cover on duff moisture. The spatial and temporal distributions of duff moisture were assessed through daily duff moisture measurements collected at regular intervals in ten 10 × 10 m plots representing a variety of canopy types and densities. Meteorological conditions ranged from very wet to very dry and resulted in duff moisture variations that were more pronounced during wet periods than in extended periods of drying. Investigations on the influence of canopy type, tree density, and tree proximity on duff moisture patterns indicated that canopy type and tree proximity are the most important factors affecting duff moisture. Interception seems to be the primary controller of duff moisture patterns with an influence at the centimetre scale. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 teacher head, 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".