Evaluation of digital and film hemispherical photography and spherical densiometry for measuring forest light environments
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.254
- Threshold uncertainty score
- 0.301
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.246 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
This study presents the results of a comparison of digital and film hemispherical photography as means of characterizing forest light environments and canopy openness. We also compared hemispherical photography to spherical densiometry. Our results showed that differences in digital image quality due to the loss of resolution that occurred when images were processed for computer analysis did not affect estimates of unweighted openness. Weighted openness and total site factor estimates were significantly higher in digital images compared with film photos. The differences between the two techniques might be a result of underexposure of the film images or differences in lens optical quality and field of view. We found densiometer measurements significantly increased in consistency with user practice and were correlated with total site factor and weighted-openness estimates derived from hemispherical photography. Digital photography was effective and more convenient and inexpensive than film cameras, but until the differences we observed are better explained, we recommend caution when comparisons are made between the two techniques. We also concluded that spherical densiometers effectively characterize forest light environments.
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.
The record
- Venue
- Canadian Journal of Forest Research
- Topic
- Remote Sensing and LiDAR Applications
- Field
- Environmental Science
- Canadian institutions
- not available
- Funders
- Andrew W. Mellon FoundationNational Science Foundation
- Keywords
- Digital photographyPhotographyOpenness to experienceOpticsConsistency (knowledge bases)Remote sensingDigital cameraDigital imageLens (geology)Digital imagingEnvironmental scienceMaterials scienceComputer scienceComputer visionGeographyArtificial intelligencePhysicsImage (mathematics)Image processingArtPsychologyVisual arts
- Has abstract in OpenAlex
- yes