Bibliographic record
Abstract
This paper demonstrates the skills, knowledge, and resources required for completing a refined MM5-CALPUFF analysis for a regional air quality study.The dispersion over the entire country using five years of MM5 meteorological data necessitates large scale computation.To assess additional computing resources, an intranet system developed by Lakes Environmental has been utilized to take advantage of idle processing cycles of all personal computers available in an organization.The distributed computing solution requires the modelling task be divided into a series of monthly calculations for each source.As partial results from participating computers are returned, they are summed to yield the final total impact in post processing.Further reduction of computational effort is achieved through application of advanced modelling techniques.Since nonlinearity of the model is insignificant for emissions involving only sulphur dioxide, as it is the case for our project, the final solution can be scaled linearly with respect to the source strength.Grouping of identical sources may also reduce some of the computation, although it is not utilized for the project.By application of innovative distributed computing solution and advanced modelling techniques, we have reduced the project life to one month, instead of one year if it were executed in a straightforward manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".