Bibliographic record
Abstract
One more calendar year comes to an end.The issue at hand marks the 4th and last quarterly issue of 2025.It also marks a somewhat 'bumpy transition' to the new review system we now use for paper reviews.Granted, there are still about 45 under review papers in the (old) Editorial Manager (EM), however, we believe that we will bring closure to the EM within the next 6 months.An encouraging observation after the 'transition' of our Journal to a peer review Open Access publication, is that the number of submissions increases, slowly, but steadily!WE are confident that we will reach, soon, previous submission levels, topping 1300 + per year!Moreover, from our side, we make every effort to speed up the review process, albeit challenges related to reviewer fatigue, timing restrictions to submit reviews, etc.Let us face it: there are so many journals and 'journals' and each one of us receives so many emails, daily, to review papers, such that it is no surprise if such emails are basically ignored -period!Regardless, please look for the 'meaningful email' sent from the Journal of Intelligent and Robotic Systems and do process the papers in your queue.We count on your help to make our Journal better.I thank each one of you, authors, reviewers,
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".