Influence of Weather Conditions on the Surface Morphology and Wetting Behaviour of Superhydrophobic Quaking Aspen Leaves
Bibliographic record
Abstract
The effects of different environmental conditions on the wetting properties and surface morphology of surperhydrophobic quaking aspen leaves harvested during the 2011 growth season are examined. During this particular season quaking aspen leaves were not able to retain their superhydrophobic properties and associated surface structure features as they have usually been able to do in other years. Representative scanning electron microscopy images and wetting property measurements of quaking aspen leaf surfaces harvested throughout this season are presented and discussed with the objective of linking weather induced environmental stresses that occurred in 2011 to the sudden and unusual reduction in non-wetting properties and drastic changes in leaf surface structure. Erosion and regeneration rates of leaf wax crystals and the impact that environmental factors can have on these are considered and used to explain the occurrence of these unexpected changes.
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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 | 0.002 |
| 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 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".