Bibliographic record
Abstract
This paper compares two methods built on a hierarchical Bayesian foundation and designed for modeling hourly ozone concentrations over the eastern United States. One, a dynamic linear state space model (DLM) that has been proposed earlier, lies in a very contemporary setting where two historical paths to temporal process models, the Kalman filter and the dynamic system with random perturbations, converge. The other, which we call the Bayesian spatial predictor (BSP), is a Bayesian alternative to the purely spatial method of kriging. The DLM as a dynamic system model has parameters that are states of the process which generate the ozone and change with time. More specifically, the model includes a time-varying site invariant mean field as well as time-varying coefficients for 24 and 12 hour diurnal cyclic components. The resulting model’s great flexibility comes at the cost of complexity, forcing the use of an MCMC approach and very time-consuming computations. Thus, the size of the DLM’s spatial domain of applicability has to be restricted and the number of monitoring sites that can be treated limited. The paper’s assessment of the DLM reveals other difficulties that point to the need to consider a less flexible competitor, a Bayesian spatial predictor (BSP). The two methods are compared in a variety of ways and overall conclusions given. In particular, the conclusions point to the BSP as the more practical alternative for spatial prediction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".