Estimating light environment in forests with a new thresholding method for hemispherical photography
Bibliographic record
Abstract
Light environment estimates derived from hemispherical photography are known to be affected by variations in sky illumination. During photo acquisition, rapid changes in sky illumination can occur and will result in changes in detected canopy gap size and frequency. Any resulting problems in image consistency will become more serious with increased time lags between setting the reference exposure and hemispherical photograph acquisition. We showed that if the camera exposure setting was kept constant during photo acquisition, the estimated diffuse transmittance would be greatly influenced by sky illumination change. We developed a new pixel thresholding method that calculated the optimal threshold value for the separation of sky and plant pixels as a function of the above-canopy photosynthetic photon flux density (PPFD). We tested the performance of our method for estimating transmittance against two established methods that assume exposure to be held constant to two stops higher than the reference exposure. Our method compensates for changes in sky illumination, producing a smaller pixel threshold value when sky illumination decreases and a larger pixel threshold value when photographs are taken under increased sky illumination. The new method achieved accurate and reproducible results, even in situations where under- or over-exposure was caused by changes in sky illumination during photo acquisition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".