Hiding in plain sight: combining field-naturalist observations and herbarium records to reveal phenological change
Bibliographic record
Abstract
As the climate warms, northern ecosystems are experiencing warmer winters and seasonal climatic shifts. Vascular plants are expected to respond to climate change by adjusting flowering or seeding periods. To determine how a native mixed-wood boreal floral assemblage has responded to warming temperatures over the 20th century, we collated historical observations made by field-naturalists as well as voucher data from the Thunder Bay region of Ontario, Canada. Combining these datasets, we performed regression analyses on 11 species of spring-flowering vascular plants to evaluate temporal trends and used spring cumulative growing degree day (sGDD0) to determine the influence of climate on flowering times. Four species showed consistent positive temporal trends (i.e., flowered later with time), while four species (three of which also demonstrated temporal trends) showed negative trends with sGDD0 (i.e., flowered earlier with an increased number of degree days above 0°C). The unexpected observation of later flowering times but predicted observation of earlier blooming with increased sGDD0 indicates that the inclusion of climate metrics may be necessary to determine the response of native vascular plants to the onset of changes in their environment. These observations were not statistically significant when field-naturalist or herbarium voucher data were analyzed separately, possibly due to low statistical power. Combining data from both sources, however, revealed common responses to climate warming among species within an ecoregion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".