Constrained classification on structured data
Bibliographic record
Abstract
Most standard learning algorithms, such as Logistic Re-gression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identi-cally distributed) data. They therefore do not work ef-fectively for tasks that involve non-i.i.d. data, such as “region segmentation”. (Eg, the “tumor vs non-tumor” labels in a medical image are correlated, in that adja-cent pixels typically have the same label.) This has mo-tivated the work in random fields, which has produced classifiers for such non-i.i.d. data that are significantly better than standard i.i.d.-based classifiers. However, these random field methods are often too slow to be trained for the tasks they were designed to solve. This paper presents a novel variant, Pseudo Conditional Ran-dom Fields (PCRFs), that is also based on i.i.d. learners, to allow efficient training but also incorporates correla-tions, like random fields. We demonstrate that this sys-tem is as accurate as other random fields variants, but significantly faster to train.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".