Observed Spring Phenology Responses in Experimental Environments (OSPREE)
Bibliographic record
Abstract
This meta-analytic database of the published literature on spring phenology (e.g., budburst or leafout) of woody species in controlled environments (e.g., growth chambers) was designed to better inform models and related forecasts of spring phenology by: (1) allowing tests of the relative effects of chilling, forcing and photoperiod (daylength) on spring phenological responses and (2) giving an overview of the design of most experimental studies to date. There are four versions of this dataset:1) ospreebb_forknb.csv: a subset of the full database focused on budburst, used in Ettinger, A.K., Chamberlain, C.J., Morales-Castilla, I., Buonaiuto, D.M., Flynn, D.F.B., Savas, T., Samaha, J.A. and Wolkovich, E.M., 2020. Winter temperatures predominate in spring phenological responses to warming. Nature Climate Change, 10(12), pp.1137-1142.2) ospree_forknb_limcue.csv: the full database, used in E.M. Wolkovich,, C.J. Chamberlain, D.M. Buonaiuto, A.K. Ettinger, I. Morales-Castilla. 2022. Limiting cues: How spring warming, winter chilling and daylength shape climate change responses. New Phytologist.3) ospreebb2019update_forknb.csv: an update to the budburst database above, which includes additional datasets added in 2019 (see Methods for details).4) ospreebbphyloms_forknb.csv: a subset of the updated budburst database above, used in Morales-Castilla, I., et al. "Phylogenetic estimates of species-level phenology improve ecological forecasting"
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".