Improving accuracy of canopy hemispherical photography by a constant threshold value derived from an unobscured overcast sky
Bibliographic record
Abstract
High image discrimination threshold values tend to be given to canopy hemispherical photographs (CHPs) with high exposure (resulting in bright images), but the effects of exposure on image threshold have been overlooked. A model canopy system was developed to precisely manipulate exposure (in relation to reference exposure measured from an unobscured overcast model sky), canopy openness, gap fragmentation and sky illumination of CHPs. We showed that there was a numerical trade-off relationship between exposure and image threshold of CHPs, whereas the last three factors had negligible effects on image threshold. A new thresholding method based on this relationship was shown to be applicable in the field and to allow users of hemispherical photography (HP) to compensate for the effects of higher exposure on CHPs with higher empirical threshold values. The advantages of this method are as follows: (i) it is an objective method; (ii) its overall performance is better than that of automatic thresholding methods throughout a wide range of canopy openness and exposure; and (iii) its efficiency of image analysis is higher than that of conventional manual and automatic thresholding methods, because all CHPs can be analysed with a constant empirical threshold value. Our results highlight the necessity of integrating both exposure and thresholding for optimal CHP results and the contribution that model canopies can make to methodological studies of HP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".