Ovular secretions as part of pollination mechanisms in conifers
Bibliographic record
Abstract
Conifers have a diversity of pollination mechanisms that assist in the capture of pollen during pollination.Pollination mechanisms can be divided into a number of general types depending on whether they have an ovular secretion that interacts with the pollen.These types include mechanisms that never have a secretion, or those that have a delayed secretion, or the most common type in which a pollination drop is formed.This review outlines the evolutionary context of ovular secretions, describes the origins of these secretions within the ovule, their function in the two types of pollination mechanisms, and details the biochemical composition of these liquids.Not only do ovular secretions provide a germination medium for pollen, but they may also play a significant part in reducing pollen pollution by foreign species.pollination mechanism / conifer / ovular secretion / pollination drop Résumé -Les sécrétions ovulaires : leurs rôles dans les mécanismes de pollinisation des conifères.Les conifères possèdent divers mécanismes de pollinisation qui aident à la capture des grains de pollen lors de la pollinisation.Ces mécanismes peuvent être classés en quelques types généraux selon qu'une sécrétion ovulaire interagissant avec le pollen existe ou non.Ces différents types comprennent des mécanismes sans sécrétion, avec sécrétion retardée ou, et c'est le type le plus répandu, avec formation d'une goutte de pollinisation.Cet article décrit le contexte de ces secretions en terme d'évolution, leurs origines ovulaires, leurs fonctions dans les deux types de mécanismes de pollinisation, et leur composition biochimique.Les sécrétions ovulaires non seulement fournissent un milieu favorable à la germination du pollen, mais peuvent aussi diminuer de façon importante la pollution pollinique due à des pollens étrangers. mécanisme de pollinisation / conifère / sécrétion / goutte de pollinisation
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".