Branch architecture in <i>Ginkgo biloba</i>: Wood anatomy and long shoot–short shoot interactions
Bibliographic record
Abstract
PREMISE: Ginkgo, centrally placed in seed plant phylogeny, is considered important in many phylogenetic and evolutionary studies. Shoot dimorphism of Ginkgo has been long noted, but no work has yet been done to evaluate the relationships between overall branch architecture and wood ring characters, shoot growth, and environmental conditions. • METHODS: Branches, sampled from similar canopy heights, were mapped with the age of each long shoot segment determined by counting annual leaf-scar series on its short shoots. Transverse sections were made for each long shoot segment and an adjacent short shoot; wood ring thickness, number of rings, and number of tracheids/ring were determined. Using branch maps, we identified wood rings for each long shoot segment to year and developmental context of each year (distal short shoot growth only vs. at least one distal long shoot). Climate data were also analyzed in conjunction with developmental context. • KEY RESULTS: Significantly thicker wood rings occur in years with distal long shoot development. The likelihood that a branch produced long shoots in a given year was lower with higher maximum annual temperature. Annual maximum temperature was negatively correlated with ring thickness in microsporangiate trees only. Annual minimum temperatures were correlated differently with ring thickness of megasporangiate and microsporangiate trees, depending on the developmental context. There were no significant effects associated with precipitation. • CONCLUSIONS: Overall, developmental context alone predicts wood ring thickness about as well as models that include temperature. This suggests that although climatic factors may be strongly correlated with wood ring data among many gymnosperm taxa, at least for Ginkgo, correlations with climate data are primarily due to changes in proportions of shoot developmental types (LS vs. SS) across branches.
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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) | 0.001 | 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".