Évaluation des facteurs associés à l'occurrence des cyanobactéries à la prise d'eau et modélisation de leur distribution spatio-temporelle
Bibliographic record
Abstract
DÉDICACE Cette thèse est dédiée à la mémoire de mon cher ami, promotionnaire et collègue de travail Elhadji NGOM, ingénieur de conception en Génie chimique (option: procédés industriels), de l'École Supérieure Polytechnique (ESP) de Dakar (SÉNÉGAL), que la terre de TOUBA lui soit légère.iv REMERCIEMENTS Je tenais à remercier le Dr. Sarah DORNER, ma directrice de recherche pour ses encouragements, sa patience, son soutien et son apport scientifique durant toute la durée de la thèse.Je remercie également les Dr. David BIRD, Tri NGUYEN-QUANG, René KAHAWITA et Michèle PRÉVOST pour leur apport scientifique remarquable et leurs encouragements.Je remercie aussi mes parents, Cheikh Abdoulaye NDONG et Seynabou FAYE, de même que mes frères et sœurs pour leur patience et leur soutien tout au long de mes études.J'accorde une mention spéciale à ma femme Ndéye Marie FAYE et ma petite fille Adja Codou NDONG pour leur patience et leur soutien.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".