Work-in-progress project report
Bibliographic record
Abstract
CESTA, the first European Campaign dedicated to MT Evaluation, is a project labelled by the French Technolangue action. CESTA provides an evaluation of six commercial and academic MT systems using a protocol set by an international panel of experts. CESTA aims at producing reusable resources and information about reliability of the metrics. Two runs will be carried out: one using the system's basic dictionary, another after terminological adaptation. Evaluation task, test material, resources, evaluation measures, metrics, will be detailed in the full paper. The protocol is the combination of a contrastive reference to: IBM "BLEU" protocol (Papineni, K., S. Roukos, T. Ward and Z. Wei-Jing, 2001); "BLANC" protocol derived from (Hartley, Rajman, 2002).; "ROUGE" protocol (Babych, Hartley, Atwell, 2003). The results of the campaign will be published in a final report and be the object of two intermediary and final workshops.
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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.146 |
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".