Indexation multidimensionnelle de bases de données capteur temps-réel et spatio-temporelles
Bibliographic record
Abstract
Les reseaux de capteurs lies a des bases de donnees sont de plus en plus frequemment utilises pour la surveillance de milieux a risques. Ces systemes sont usuellement composes d'un ensemble de capteurs envoyant les mesures effectuees vers une base de donnees centralisee. La frequence des mesures aussi bien que les besoins des utilisateurs imposent a la base des contraintes temps-reel douces, tout en valorisant les donnees les plus recentes. Quant au besoin semi-generalise d'acceder aux donnees en fonction de criteres spatiaux, il impose a son tour des caracteristiques spatiotemporelles. Afin de repondre aux specificites de ces systemes, cet article propose deux methodes d’indexation de donnees. La premiere, dediee a l'indexation d'un grand nombre de donnees issues de capteurs fixes se nomme le PoTree. Son evolution, le PasTree, privilegie la gestion de l'agilite des capteurs et la diversification des methodes d’interrogation
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".