Spatial Bootstrapping of High Spatial and Temporal Resolution Thermal Infrared Imagery: A Canopy Wetness Case Study
Bibliographic record
Abstract
We measure and compare temperatures of dry and wet plant canopies with a high spatial resolution thermal imaging camera. We then evaluate measurement variability and how this changes during evapotranspiration. Our assessments examine mean temperatures and their variability under constant evapotranspiration conditions. We partition our study by time since wetting (Tn), plant replicate (Pn), moisture condition (Mn), measurement height above target (Hn), and observation day (Dn). Treatments involved wetting half of each plant’s canopy, imaging each replicate from two heights (1 and 2 m) nadir to the canopy, and then assessing temperatures within wet and dry zone masks. Images were acquired every 5 minutes for 1 hour and treatments were replicated over two days. Spatial bootstrapping (SBS) was performed independently within each zone for each treatment using 50 random placements of a 3×3 pixel window. Our methods show that natural heterogeneity in dry canopies presents less variable temperatures than wetted canopies, where there was an influence of variable wetting. The allotted treatment duration successfully permits full evaporation of water from the wetted canopy and therefore allowed us to identify the point at which wetted zones returned to a localized equilibrium with the dry zones.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".