Evaluation of site quality from aerial photographs of the University of British Columbia Research Forest, Haney, B.C.
Bibliographic record
Abstract
Classification of site of forest land is possible on aerial photographs. This classification can be based on topographic features, physiographic features, forest cover types, or on their combinations. Aerial photographs of the University Research Forest were typed using the following topographic features: exposure, percentage of slope, shape in profile, and shape in contour. Data on topographic and physiographic features were collected on 238 sample plots within topographic types in 30-year-old stands, on 83 permanent sample plots in 70-year-old stands, and on 26 sample plots in old-growth stands. Both graphical and mathematical analyses were carried out to determine relationships among site index and thirteen site factors. Simple correlation coefficients for site index of each of 320 plots were highly significant for each of local and general position on slope, per cent of slope, elevation, soil depth, moisture regime, permeability, soil texture, and thickness of A₂ later. Shape in profile was significantly associated with site index. Aspect, shape in contour, and thickness of the humus layer were not significantly associated with site index. The best of the single factors was moisture regime, but use of this by itself could only account for 20 per cent of the variation inplot site indices. Linear multiple-regression equations were computed to estimate site index from various combinations of topographic and physiographic variables. These equations were not used further in this study for determination of site index because of their relatively high standard error of estimate; however, several potentially useful equations were recognized. The best multiple-regression equation was highly significant statistically but accounted for only 31 per cent of the variation in plot site index. It included aspect, local and general position on slope, per cent of slope, shape in profile, elevation, and moisture regime. A procedure was developed to estimate site indices directly from aerial photographs by stereoscopic examination. Photo-estimation of site index was much more accurate than the computed equations based on all data collected in the field. Standard errors of estimate were reduced from 23 feet to 16 feet by direct estimation of site index. Regression equations were developed for conversion of site index of Douglas fir, western hemlock, and western red cedar from one species to another and to the average of all three species. Site maps were prepared for the 30-year-old stands which had not been mapped in the 1950 inventory of the University Research Forest. Preliminary site and forest cover types were recognized and general stand and stock tables were developed to describe these 30-year-old stands.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".