Refining tree recruitment models
Bibliographic record
Abstract
We used a micrometeorological dispersal model to simulate seed and seedling distributions derived from subcanopy balsam fir (Abies balsamea (L.) Mill.) source trees in a trembling aspen (Populus tremuloides Michx.) dominated forest. Our first objective was to determine the effect of substituting basal area for cone production as a proxy for seed output. The results showed that the r2 from the regression of predicted versus observed densities increased by ~5% for seeds and ~15% for seedling simulations. Our second objective was to determine the effects of changing the median horizontal wind speed. The median speed in this forest environment varies according to the proportion of leaves abscised. For values of the median expected wind speed between the extremes of leafless and full-canopy forests, the r2 of predicted versus observed varied between 0.35 and 0.49 for seeds and between 0.33 and 0.62 for seedling simulations. We demonstrated that the simple one-dimensional model can have added precision if the dispersal parameters are chosen so as to allow more fine-scale variation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".