Estimates of past and future ozone trends from multimodel simulations using a flexible smoothing spline methodology
Bibliographic record
Abstract
A novel additive model analysis of multimodel trends is presented. The approach is motivated by, and particularly suited to, the analysis of multimodel time series of varying length. This Time series Additive Model (TSAM) approach consists of three distinct steps: estimation of individual model trends, baseline adjustment of the trends, and the weighted combination of the individual model trends to produce a multimodel trend (MMT) estimate. The baseline adjustment step is not an essential ingredient of the TSAM but is included to reduce model spread. The association of the TSAM approach with a probabilistic model allows trend estimates to be used to make formal inference (e.g., calculation of confidence and prediction intervals). The method is applied to the analysis of multimodel ozone time series of varying lengths as were considered for the 2006 Scientific Assessment of Ozone Depletion. The advantages of the TSAM approach are demonstrated to include the production of smooth trend estimates out to the ends of the time series, the ability to model explicitly interannual variability about the trend estimate, and the ability to make rigorous probability statements. Calculated ozone return dates are consistent with previous qualitative estimates, but the more quantitative analysis provided by the MMT is expected to allow such data sets to be better utilized by the community and policy makers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".