Data from: Spot size, distance, and emissivity errors in field applications of infrared thermography
Bibliographic record
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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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".