Progressive deterioration of pollination service detected in a 17‐year study vanishes in a 26‐year study
Bibliographic record
Abstract
Widespread reports of declining populations of pollinators have raised concerns that plant populations may be incurring increasing shortfalls in pollination, but few studies have measured pollination deficits over enough seasons to detect such changes. I have conducted pollen-supplementation experiments in a wild population of the glacier lily (Erythronium grandiflorum, Liliaceae) from 1993 to 2018. Pollination deficits were estimated by comparing the fruit set of hand-pollinated, single-flowered plants to that of open-pollinated controls. For a subset of years, seed set data were also available. A previous publication reported a significant deterioration of pollination in this population from 1993 to 2009, and suggested phenological dislocation as a possible cause. That deterioration is no longer evident in the longer-term data set. Very long time series may be necessary to detect temporal trends in pollination service. This population consistently experiences stronger pollination deficits before its flowering peak than after. This heterogeneity suggests caution in characterizing a population as pollination-limited or not, even within a single season.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".