Test of the temperature difference model predicting masting behavior
Bibliographic record
Abstract
The differential temperature (ΔT) model, based on the assumption that masting plants respond to the difference in the temperatures during the growing seasons 1 and 2 years prior to seed production, has recently been proposed to explain the proximate factor of masting. In this study, we used a 28-year series of data on pollen cone and seed production in Cryptomeria japonica D. Don in Japan and compared several models based on temperatures and resources to test whether ΔT acts as a cue or is a proxy for resource limitation. Of all the models tested, models including ΔT, previous summer absolute temperature Tn–1, and reproduction in the previous year provided the best fit. The number of pollen cones and seeds produced was proportional to the difference in the mean daily maximum temperature during June to August between the preceding two years. In addition, to test whether the double mast events in consecutive years was less common than consecutive warm summers, we used our dataset and 12 datasets of pollen dispersal of 17 years or longer of C. japonica in Japan. Although consecutive warm summers occurred in 4.4% of pairs, double mast events occurred in 1.0%. The ΔT model was a considerably better predictor of the rare phenomenon of double mast events, which occur only after a specific sequence of cold–moderate–hot absolute summer temperatures. Thus, ΔT acts as cue for masting in C. japonica.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".