Une version pondérée de la factorisation matricielle non négative pour l'identification de sources de particules atmosphériques. Application au littoral de la mer du Nord
Bibliographic record
Abstract
This paper focuses on approximate factorization technics of the measures matrix X(n samples,m sensors) into a contribution matrix G and a profile matrix F. Findin g G and F can be made through specific factorization methods (PMF 1 et NMF 2 ) that look alternatively for the best matrix G and F. In this article, a NMF based algorithm is proposed. It e nables to take advantage of the NMF method on the one hand, and on the other hand to impr ove the accuracy of the results. This algorithm is used on concentration measures of airbor ne particulate coming from the area of Dunkerque coast in periods mainly affected by marine so urces. MOTS-CLES : Pollution de l'Air, Diagnostic, Factorisation matricielle approchee.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".