Foliage influences forced convection heat transfer in conifer branches and buds
Bibliographic record
Abstract
Conifer foliage structures affect branch and bud temperature by altering the development and convective resistance of the thermal boundary layer. This paper examines foliage effects on forced convection in branches and buds of Picea glauca (Moench) Voss and Pinus contorta Dougl. Ex. Loud., two species that represent the range of variation in foliage structure among conifers. Forced convection is characterized by a power law relating Nusselt (heat transfer) and Reynolds (boundary layer development) numbers. Data were collected in a laminar flow wind tunnel for free stream velocities of 0.16-6.95 m s(-1). Scaling parameters were compared against literature values for silver cast branch replicas, a bed of real foliage, cylinders, and tube banks. Foliage structures reduced Nusselt numbers (heat transfer) relative to cylinders, which are typically used to approximate leafless branches and buds. Significantly different scaling relationships were observed for all foliage structures considered. Forced convection scaling relationships varied with foliage structure. The scaling relationships reported here account for variation within populations of branches and buds and can be used to characterize forced convection in a forest 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 | 0.000 |
| 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) | 0.000 | 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 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".