Within-crown cone production patterns dependent on cone productivities in <i>Pinus densiflora</i>: effects of vertically differential, pollination-related, cone-growing conditions
Bibliographic record
Abstract
Although the variations in within-crown cone production have mainly been associated with resource availability, trees with relatively fewer cones allocate more cones to the optimal vertical layer for cone production than their expected resource availability suggests. We investigated the number of cones (NCone) per branch basal area (BBA) and the proportion of cones (PCone) in the three crown layers (upper, middle, and lower) for 72 Pinus densiflora Sieb. et Zucc. clones in 2004 and 2005. We also measured cone characteristics in each layer to infer their resource or pollen availability. Further, in 2006 we conducted pollination experiments for another 19 P. densiflora ramets, manipulating pollen quality (open-pollinated, self-pollinated (selfed), and polycrossed) in their two crown layers (upper and lower), and examined how pollen quality could affect the among-layer differences. Among 72 clones investigated, PCone in the upper crown layer was significantly greater, with a decrease of the total NCone per BBA; at that time, the seed/ovule ratio (S/O) in the upper crown layer cones was generally greater than that found in the middle and lower crown layers. In the pollination experiments, self-pollination resulted in a decreased S/O in the cones, regardless of the crown layer: S/O in the selfed cones, which were pollinated with controlled-quality pollen, did not differ between the upper and lower layers. Clones with relatively fewer cones allocated more resources to producing cones in the optimal cone-producing layer (upper crown layer) than would be expected from their biomass allocation, suggesting that optimal cone production is the result of a pollination-related factor.
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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 | 0.001 | 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".