A new herbarium‐based method for reconstructing the phenology of plant species across large areas
Bibliographic record
Abstract
Phenological data have recently emerged as particularly effective tools for studying the impact of climate change on plants, but long phenological records are rare. The lack of phenological observations can nevertheless be filled by herbarium specimens as long as some correction procedures are applied to take into account the different climatic conditions associated with sampling locations. In this study, we propose a new herbarium-based method for reconstructing the flowering dates of plant species that have been collected across large areas. Coltsfoot (Tussilago farfara L.) specimens from southern Quebec were used to test the method. Flowering dates for coltsfoot herbarium specimens were adjusted according to the date of disappearance of snow cover in the region where they were collected and compared using a reference point (the date of earliest snowmelt). In southern Quebec, coltsfoot blooms earlier at present (15-31 d) than during the first part of the 20th century. This phenomenon is likely associated with the climate warming trends recorded in this region in the last century, especially during the last three decades when the month of April became warmer, thereby favoring very early-flowering cases. The earlier flowering of coltsfoot is, however, only noticeable in large urban areas (Montreal, Quebec City), suggesting a strong urban heat island effect on the flowering of this plant. Herbarium specimens are useful phenological indicators; however, the databases should be carefully examined prior to analysis to detect biases or trends associated with sampling locations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".