Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In multiclass classification, one faces greater uncertainty when the data fall near the decision boundary. To reduce the uncertainty, one can wait and collect more data, but this invariably delays the decision. How can one make an accurate classification as quickly as possible? The solution requires a multiclass generalization of Wald’s sequential hypothesis testing, but the standard formulation is intractable because of the curse of dimensionality in dynamic programming. In “Optimal Sequential Multiclass Diagnosis,” Wang shows that, in a broad class of practical problems, the reachable state space is often restricted on, or near, a set of low-dimensional, time-dependent manifolds. After understanding the key drivers of sparsity, the author develops a new solution framework that uses a low-dimensional statistic to reconstruct the high-dimensional state. This framework circumvents the curse of dimensionality, allowing efficient computation of the optimal or near-optimal policies for quickest classification with large numbers of classes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 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 it