Mutual regularity of spring phenology of some boreal tree species: predicting with other species and phenological models
Bibliographic record
Abstract
Phenological models constructed from observations of one species are often extrapolated to predict the phenology of other species. In this study, time series of the flowering and bud burst of several boreal zone trees were collected. The observation series were regressed against each other in pairs to test mutual variation. In addition, two models of phenology, one based on chilling requirement, and the other assuming ontogenetic development starting from a signal from the light climate were fitted to the phenological time series. The root mean square error of the regression models forecasting one observation series with another was quite constant for all event pairs, and the smaller the closer in time the events took place. It seems that different plant species react to climate variables in a similar manner, thus the use of the same models for different species and phenomena is justified. The light climate triggered model, albeit more simple, gave estimates that were better than those of the regression models between the events, while the average residuals of the estimates from the chilling triggered model were considerably larger. It was concluded that the chilling requirement component was redundant for prediction accuracy in the spring phenology models of boreal trees.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".