Bibliographic record
Abstract
compurgation is one of the way to prove murder which is applicable in spite of the besmirch and it is as follows if the murder happened and anyone did not confess to murder and the heir's blood (the family of the victim) was unable to acceptable appeal to his witnesses case and vigor in case of the murder was committed by a person or group of people, as besmirch which include the suspicion of ruler to the telling truth by defendant, was available, defendant with his relatives in case of premeditated murder swear fifty oaths and in the case of quasi-intentional murder swear twenty-five oath for purely fault and to be proved his claim.Otherwise defendant runs the besmirch and exonerate.Since the requirement of principle, In the case of acquainting in realization of the besmirch subject is absent and on the other hand oath of the claimant is denial according to the rules of evidence to the contender, it is Contrary to rule.Author intends to considered the besmirch and its legitimacy to investigate the nature, Quality, installer quantity and conditions of them in this paper after presenting compurgation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.970 | 0.964 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".