L’intelligence artificielle au service du monde des fusions acquisitions : la plateforme Sealk
Bibliographic record
Abstract
L’émergence des startups dans les économies mondiales et l’arrivée massive des fonds d’investissement ont bousculé le monde des affaires depuis les années 2000, posant le problème de l’information d’une manière aiguë. Dans ce monde prolifique et bouillonnant, le réseau personnel et les bases de données manuelles ne suffisent plus : les nouvelles technologies, en particulier l’intelligence artificielle, viennent révolutionner la recherche de cibles dans une stratégie d’acquisition d’entreprises (M&A). Ainsi, la plateforme Sealk (en phase de pré-commercialisation), utilise l’IA pour collecter des informations récentes et pertinentes sur les startups ou les PME/PMI, mais aussi de manière prédictive : à partir de l’analyse des opérations de fusion ou d’acquisition, tel grand groupe peut anticiper - prévoir quelle start-up acquérir s’il veut ne pas prendre de retard sur ses concurrents ; inversement, une start-up peut chercher à quels grands groupes s’adosser. Cette solution puissante et originale, destinée aux banquiers d’affaires, aux fonds d’investissement et aux grands groupes, ne requiert aucune connaissance en programmation.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".