Riparian tree fall directionality and modeling large wood recruitment to streams
Bibliographic record
Abstract
Directionality of tree fall in riparian forests can strongly influence predictions of large wood recruitment to streams, yet accuracy of this model parameter has rarely been assessed with field data. We measured fall directions of 1202 riparian trees distributed among 21 stream sites across the Pacific Northwest, USA. Fall directions were oriented towards the stream at 16 sites, upstream at four sites, and not distinguishable from random at one site. Average tree fall direction across sites was correlated with valley constraint (Spearman r = 0.53; p = 0.02), but variability of fall directions was not correlated with this variable. When grouped by species (six conifers and one deciduous), individual trees exhibited stronger tendency to have fallen towards the channel on steep hillslopes (>40%) than on moderately sloped landforms (<40%). Integration of field data into an established recruitment model indicated that 1.5 to 2.4 times more large wood (by number of tree boles) would be recruited to stream reaches with steep hillslopes than to reaches with moderate side slopes or flat banks, if riparian forest conditions are assumed to be constant. We conclude that stream valley topography should be considered in models that use tree fall directions in predictions of large wood recruitment to streams.
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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 | 0.001 | 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 | 0.000 |
| 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 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".