Modeling individual conifer seed shape as a sum of fused partial ellipsoids
Bibliographic record
Abstract
Because of the importance of seed surface area, volume, and fill to hydraulic and thermal exchanges with the soil, mechanistic simulation of seed physiological processes associated with tree migration dynamics and the spread of invasive species require accurate equations to model seed shape. Seed dimensions have previously been described with measurements of the three principal axes, assuming an implied single ellipsoid. However, conifer seeds often exhibit anisotropy that results from bilaterally symmetric pairing on cone scales. We developed a method for measuring and modeling conifer seed shape as a sum of 2jpartial ellipsoids fused at their equators, where j = 0, …, 3. We demonstrate the use of the methods in the study of shape characteristics of ponderosa pine (Pinus ponderosa P.& C. Lawson) seeds from four families in Montana and among commercial lots of ponderosa pine, lodgepole pine (Pinus contorta Dougl. ex Loud.), and Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco). The shapes of 92%, 73%, and 47% of seeds in commercial lots studied had eight unique ellipsoids when classified with 1%, 5%, and 10% difference classification rules, respectively. Ponderosa pine seeds with longer minor axes were less well filled with storage reserves. Three-dimensional surface areas of lodgepole and ponderosa pine were approximately 2 and 3.4 times larger, respectively, than previously reported one-sided surface areas.
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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 | 0.000 |
| 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)
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".