Model-based local thresholding for canopy hemispherical photography
Bibliographic record
Abstract
Canopy hemispherical photography (HP) is widely used to estimate forest structural variables. To achieve good results with HP, a classification algorithm is needed to produce binary images to accurately estimate the gap fraction. Our aim was to develop a local thresholding method for binarizing carefully acquired hemispherical photographs. The method was implemented in the R package “caiman”. Working with photographs of artificial structures and using a linear model, our method turns the cumbersome problem of finding the optimal threshold value into a simpler one, which is estimating the digital number (DN) of the sky. Using hemispherical photographs of a deciduous forest, we compared our method with several standard and state-of-the-art binarization techniques. Our method was as accurate as the best-tested binarization techniques, regardless of the exposure, as long as it was between 0 and 2 stops over the open sky auto-exposure. Moreover, our method did not require knowing the exact relative exposure. Intending to balance accuracy and practicality, we mapped the sky DN using the values extracted from gaps. However, we discussed whether a more accurate but less practical way to map sky DN could provide, along with our method, a new benchmark.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".