Development of a new phenological model based on the carbon balance of tree in boreal conifers
Bibliographic record
Abstract
Traditional phenological models use the concepts of chilling and thermal forcing (temperature sum or degree-days) to predict buds break. Even if new model formulations get more sophisticated with time, the bases of phenological model still rely on the effect of the time of chilling and forcing temperature in interaction, or not, with photoperiod. Because of the increasing impact of climate or other related biotic or abiotic stressors, a model with more biological support is urgently needed in order to accurately predict bud break. We have developed and calibrated a new mechanistic model that is based on the physiological processes taking place before and during budbreak in several conifers species. This model describes the phenology and growth dynamics of a conifer branch as representative of the whole tree. As a general assumption, we assume that phenology will be driven by the carbon status, which is closely related to the annual cycle of dormancy – activity state through the year and to the environmental variables. The carbon balance of a branch was thus modelled i) from autumn to winter–when aboveground parts exhibit cold acclimation and dormancy– and ii) from winter to spring and summer –when deacclimation and growth resumption occurs. After being calibrated in a field experiment, the model was tested across a large area in Québec (Canada), based on observed phenological data. For the 20 field sites in Quebec, the model proved to be accurate in predicting the date of budbreak with an average error of ±3.8 days (R2=0.72). This model also allowed us to better understand the effects of winter and spring temperature on bud burst, offering new simulation perspectives under global warming and insect defoliation.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".