Bibliographic record
Abstract
Two methods for needle area estimation were compared for Pinus ponderosa Dougl. ex P. Laws. & C. Laws., and models were developed to predict total, projected, and abaxial areas. Areas of needles were determined by using video capture image analysis procedures (VCIA) and by direct measurement of needle sections. VCIA area estimates were 4060% less than abaxial areas determined from direct measurements. Allometric models fit to VCIA area and mean needle width (Wv) explained 96% of the variation in measured sample areas; models omitting Wv explained 91% of the variation. Predictions for independently collected validation data were somewhat poorer and slightly biased but had similar residual patterns. Allometric models fit to midneedle width and total needle length explained 99% of the variation in directly measured needle areas, with root mean square error equal to 2% of the mean measured areas. Results were similar for the validation data. For both models, final parameters were estimated from the combined data. It is shown that fascicle areas estimated from predictions for the middle-sized needles are nearly as accurate as estimates based on measurements for entire fascicles. Direct measurement of needles is more portable than VCIA and provides more accurate needle area estimates with less measurement effort.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".