May your New Year be happy and prosperous with “Thrombosis and Haemostasis”
Bibliographic record
Abstract
Times are changing; values undergo reestimation in our traditional new year’s editorial, as with previous years (1–3). At the end of 2015, we are again approaching the mark of 1000 submissions, a workload that we are gladly getting used to. We are honoured that our authors and readers remain confident in our journal and we from our side we continue do our best to keep and treasure their confidence. In this aspect, we have now further reduced the average time for first editorial decision to 20 days. Our current Editorial Board remains an effective team. Valuable contributions and constructive suggestions of our Section Editors have been fundamental for fruitful discussions during the Editorial Board meeting in Toronto on how to achieve our goals to further improve the journal, particularly with regards to the impact factor of T&H. Despite having an increased in total citations and visibility, our impact factor is currently at 4.98, reflecting the larger number of papers published. We are confident that our focused priorities will help us achieve a turnaround for our impact factor trend.
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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.102 | 0.076 |
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".