Bibliographic record
Abstract
Abstract To understand the basic characteristics of the observed S-shaped wind profile and the exponential flux profile within forest canopies, three hypotheses are postulated. The relationship between these fundamental profiles is well established by combining the postulated hypotheses with momentum equations. Robust agreements between theoretical predictions and observations indicate that the nature of momentum transfer within canopies can be well understood by combining the postulated hypotheses and momentum equations. The exponential Reynolds stress profiles were successfully predicted by the leaf area index (LAI) profile alone. The characteristics of the S-shaped wind profile were theoretically explained by the plant morphology and local drag coefficient distribution. Predictions of maximum drag coefficient were located around the maximum leaf area level for most forest canopies but lower than the maximum leaf area level for a corn canopy. A universal relationship of the Reynolds stress between the top and bottom of the canopy is predicted for all canopies. This universal relationship can be used to understand what percentage of the Reynolds stress at the top of canopy is absorbed by the whole canopy layer from the observed LAI values alone. All of these predictions are consistent with the conclusions from dimensional analysis and satisfy the continuity requirement of Reynolds stress, mean wind speed, and local drag coefficient at the top of canopy.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".