The use of multiple measurement techniques to refine estimates of conifer needle geometry
Bibliographic record
Abstract
Knowledge of foliar surface area is important in many fields, but estimating the area of nonflat conifer needles is difficult. The primary goal of this study was to use optical scanning and immersion methods to test and refine the standard cross-sectional geometries assumed for black spruce (Picea mariana (Mill.) BSP) and jack pine (Pinus banksiana Lamb.) needles. Projected leaf area (PLA, measured using a flatbed scanner), and hemisurface leaf area (HSLA, estimated from water immersion) were compared for conifer samples from a 37-year-old even-aged stand in northern Manitoba, Canada. The HSLAPLA relationship was used to infer information about needle cross-sectional geometry after assuming a basic form (rhombus for black spruce and hemiellipse for jack pine). The cross section of black spruce needles was best approximated as a rhombus with a major/minor diagonal ratio of 1.35. Jack pine needles were best described by a hemiellipse with major/minor axis ratio of 1.30. Minor but incorrect assumptions of needle cross-sectional geometry resulted in foliar area errors of 68% using scanning methods and 12% using immersion methods. Simple equations are presented to calculate hemisurface needle area from volume or projected needle area based on these refined parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".