Bibliographic record
Abstract
I wish you, wholeheartedly, a Happy and Prosperous New Year; health and happiness for all.I am pleased and honored to continue working with you, and I thank you for your continuous cooperation and support.Together, we will continue improving our journal with the aim to make it one of the strongest in intelligent systems and robotics.Granted, we witness a major shift towards 'autonomous systems' and 'unmanned systems'.So be it!We have worked hard to register JINT as a premier publication in both areas.We start the 2024 year stronger than ever before.We received and processed, in 2023, 1354 contributed and invited papers.This is a new record -we received and processed 1332 papers in 2022.What is important to state is that after JINT switched, on August 1, to an Open Access publication, 383 contributed papers were submitted to our journal.Our commitment to the peer review process remains the same; we will publish the best quality papers following a very thorough review process.Please note that since the 2024 calendar year is, basically, the first in which JINT has switched to an Open Access publication, this year, we will 'combine' issues into quarterly ones -thus we will publish four issues of contributed papers.However, Topical Collections will be published separately, in addition to the contributed paper issues.There is no 'upper bound' on Topical Collection issues.This decision has been made with the objective to monitor submission numbers and reevaluate the situation towards the fall of 2024.We also have a diverse Editorial Board with complementary expertise that allows for detailed paper evaluations and reviews.We commit to continue working hard to speed up the paper review cycle.This is the least we can do for you, the authors, and readers.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.235 | 0.219 |
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".