Selecting and testing an instrument for surveying stream shade
Bibliographic record
Abstract
We evaluated the suitability of several different instruments for surveying stream shade, selected one as most suitable for our purposes, and tested its accuracy. Five different operators used the instrument to estimate shade as angular canopy density (ACD), canopy density above 60°,, and canopy density above 80° in two plots—one in a mixed-age coniferous stand and one in a mixed-age deciduous stand. We compared operator estimates (ocular method) with measurements from fisheye photographs (computer-fisheye method). In a random coefficients regression model, the effect of “plot” on regression slopes and intercepts was not significant at α = 0.05. The regression line for ACD by the ocular method versus the computerfisheye method had a slope of 0.87 and an intercept of 0.02. The slope was significantly different from 1 at α = 0.05, indicating a tendency for human operators to underestimate ACD. Estimates of mean ACD on the two plots by individual operators were 2–11 percentage points lower, respectively, than mean ACD calculated from fisheye photos and the effect of operator was highly significant (ρ < 0.0001). Operators who received 45 minutes of training performed better than did an operator who received 15 minutes of training. Results suggest that operator variability is a large potential source of error in ocular estimates and that an investment of at least 1 hour of formal training may be worthwhile. The errors associated with any ocular-type canopy density measuring instrument should be documented before it is used to make statistical inferences.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".