Bibliographic record
Abstract
Since the 1960s, canopy photography has been widely used in forestry. Hemispherical photography has been the most widely used technique, but a great drawback of this method is its perceived sensitivity to hemispherical image acquisition and processing. Over the last decade, several alternative photographic approaches using restricted view angle have been proposed. Cover photography acquired via a normal lens was the first of the recently introduced photographic techniques. Use of a restricted view (often fixed) lens has subsequently contributed to the extension of canopy photography to new sensors and platforms, which ultimately have provided answers to some previous challenges regarding within-crown clumping correction, isolated and urban tree measurements, understory assessment, operational leaf inclination angle measurements, and phenological monitoring. This study provides a comprehensive review of the use of canopy photography in forestry and describes the theory and definitions of the variables used to quantify canopy structure. A case study is presented to illustrate and compare the different features and performance of the existing overstory photographic techniques; the results make it possible to suggest sampling strategies for consistent overstory canopy photographic measurements. Emerging operational fields of canopy photography are also described and discussed.
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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".