What factors can influence the reproductive phenology of Neotropical <i>Piper</i> species (Piperaceae) in a semi-deciduous seasonal forest?
Bibliographic record
Abstract
Plant phenophases can be modulated by abiotic factors as well as by evolutionary history. We tested the influence of factors shaping the reproductive phenology of 17 co-occurring Piper species in a semi-deciduous seasonal forest in southeastern Brazil over a 12 month period. We describe the phenology, applying circular statistics to the flowering and fruiting phenophases for each species. Mantel correlation tests were conducted to investigate the role of phylogeny in phenological responses, and the influence of abiotic variables (temperature, rainfall, and day length) was analyzed using generalized linear models. Additionally, we tested whether the presence of latent flower buds influenced flowering and fruiting times. The phenological variation across species of Piper in the reproductive stages was not phylogenetically structured. Flowering and fruiting occurred throughout the year, but higher seasonality was detected in the flowering phase, which positively correlated with long days (∼13 h). The flowering phase was shorter and occurred earlier in Piper species with latent flower buds than in species without them, probably because these species are better adjusted to respond when climate conditions are favorable for flower anthesis. Thus, abiotic factors and the presence of latent flower buds shape the reproductive phenology of Piper species.
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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 | 0.001 |
| 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".