Flowering phenology, fruiting success and progressive deterioration of pollination in an early-flowering geophyte
Bibliographic record
Abstract
Spatio-temporal patterns of snowmelt and flowering times affect fruiting success in Erythronium grandiflorum Pursh (Liliaceae) in subalpine western Colorado, USA. From 1990 to 1995, I measured the consistency across years of snowmelt patterns and flowering times along a permanent transect. In most years since 1993, I have monitored fruit set in temporal cohorts (early- to late-flowering groups of plants) at one site. To assess 'pollination limitation', I have also conducted supplemental hand-pollination experiments at various times through the blooming season. The onset of blooming is determined by snowmelt, with the earliest years starting a month before the latest years owing to variation in winter snowpack accumulation. Fruit set is diminished or prevented entirely by killing frosts in some years, most frequently but not exclusively for the earlier cohorts. When frosts do not limit fruit set, pollination limitation is frequent, especially in the earlier cohorts. Pollination limitation is strongest for middle cohorts: it tends to be negated by frost in early cohorts and ameliorated by continuing emergence of bumble-bee queens in later cohorts. This lily appears to be poorly synchronized with its pollinators. Across the years of the study, pollination limitation appears to be increasing, perhaps because the synchronization is getting worse.
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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".