m-NLP inference models using simulation and regression techniques
Bibliographic record
Abstract
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Space Physics. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionm-NLP inference models using simulation and regression techniquesAuthorsGuangdongLiuiDSigvaldMarholmAndersEklundLasse Boy NovockClauseniDRichardMarchandiDSee all authors Guangdong LiuiDCorresponding Author• Submitting AuthorUniversity of AlbertaiDhttps://orcid.org/0000-0002-3355-8540view email addressThe email was not providedcopy email addressSigvald MarholmUniversity Of Osloview email addressThe email was not providedcopy email addressAnders EklundSINTEF Industryview email addressThe email was not providedcopy email addressLasse Boy Novock ClauseniDUniversity of OsloiDhttps://orcid.org/0000-0003-0746-1646view email addressThe email was not providedcopy email addressRichard MarchandiDUniversity of AlbertaiDhttps://orcid.org/0000-0002-5062-7528view email addressThe email was not providedcopy email address
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".