Bibliographic record
Abstract
Plant reproduction is highly susceptible to temperature stress. The development of the male gametophyte in particular represents a critical element in the reproductive cycle with high sensitivity to elevated temperatures. Various methods have been used to test the effect of temperature stress on pollen performance or to determine the degree of susceptibility of given species and genotypes. The information gained informs the development of new crop varieties suited to grow under warmer conditions arising through climate change and facilitates predicting the behaviour of natural populations under these conditions. The characterization of pollen performance typically employs the terms 'pollen viability' and 'pollen vigour', which, however, are not necessarily used consistently across studies. Pollen viability is a nominal parameter and is often assayed relying on cellular features as proxy to infer the capability of pollen grains to germinate and complete double fertilization. Alternatively, pollen germination can be determined through in vitro growth assays, or by monitoring the ability of pollen tubes to complete different progamic steps in vivo (ability to reach an ovule, release sperm cells, lead to seed set). Pollen vigour is an ordinal parameter that describes pollen tube growth rate or the efficiency of pollen tube growth as inferred by its morphology or growth pattern. To ensure consistent and relevant terminology, this review defines these terms and summarizes the methodologies used to assess them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".