Optimisation des alliages thermoélectriques de type N à base de tellurure de bismuth pour des applications de génération d'énergie
Bibliographic record
Abstract
Points théoriques préliminaires -- Effet Seebeck -- Fonctionnement d'un couple thermoélectrique en mode générateur -- Alliages à base de tellurure de bismuth -- Effet Seebeck et développement de matériaux thermoélectriques -- Design des modules thermoélectriques -- Optimisation des alliages à base de tellurure bismuth -- Protocole expérimental -- Métallurgie des poudres -- Caractérisation des échantillons -- Article présenté au Journal of electronic Materials, "Extruded Bismuth telluride based N-type alloys for waste heat thermoelectric recovery applications" -- Experimental procedure -- Results -- Résultats complémentaires et discussion générale -- Influence de la concentration de porteurs -- Calcul de masse effective -- Évolution de la mobilité des porteurs -- Conductivité thermique totale -- Conductivité thermique du réseau -- Limites d'utilisation des alliages de tellure de bismuth.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".