Spot size, distance and emissivity errors in field applications of infrared thermography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Infrared thermography is increasingly emerging as an analytical approach within the thermal ecology research community, providing unique and rapid temperature information crucial to understanding how plants and animals exchange heat with their environment. What is difficult to appreciate are the numerous ways in which thermography may still yield inaccurate ( i.e . deviation from the ‘correct’ value) information if certain tenets are not followed. In this paper, we examine, demonstrate and discuss these tenets with an aim to provide methodological advice to ecologists interested in employing thermography. We found that spot size and distance strongly influenced the surface temperature estimates of known, calibrated temperature sources, with similar results observed in maximum eye temperature measurements in wild birds. We also report on how the angle of incidence affects the apparent emissivity of various biological surfaces (fur, feather, skin, leaves), another source of uncertainty in thermography. The variation in temperature caused by variation in distance and uncertainty in emissivity are large enough to raise flags for field applications of thermography where accuracy is necessary but control over study subjects is limited. Since accurate emissivity and distance parameters are crucial to thermography calculations, our results should serve as a framework to assist ecologists in better experimental design with respect to the use of thermography.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it