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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.017 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".