Modelling the temporal variation in the seed production of North American trees
Bibliographic record
Abstract
Many aspects of temporal variation in tree seed production (e.g., the proability distribution, periodicity, uni modality) are poorly understood. In this paper, we used 32 annual seed production records from 22 species to show that there are no discernible endogenous cycles, and there is a modest (but seldom significant) tendency for a high seed production year to be followed by an unusually low production year. Finally, we found that all of the records conformed to a single lognormal probability distribution, although our ability to discriminate among species, given short and extremely variable records, is admittedly very limited. We used the lognormal to develop the distribution of the sums of local seed production events (summed across 4 years) as an aid in predicting postharvest or postfire tree regeneration success. Our conclusion is that reliable (defined as 90% of the time) adequate stocking at the edge of an area source requires that the species of interest must comprise a very large fraction of the total basal area per area. Indeed, if the species constitutes less than about 50% of the source, neither burns nor even very narrow strip cuts will be reliably stocked.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".