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
The use of local likelihood methods (Tibshirani and Hastie 1987; Loader 1999) in the presence of data that are either interval or area censored leads naturally to the consideration of EM-type strategies, or rather local-EM algorithms. In this article we consider a class of local-EM algorithms suitable for density or intensity estimation in the temporal or spatial context. We demonstrate that using a piecewise constant density function at the E-step results in the algorithm collapsing explicitly into an EMS algorithm of the type considered by Silverman et al. (1990). This discovery has two advantages. Identifying a relationship between local likelihood and the EMS algorithm means the former provides a natural context for the latter further to that given by Nychka (1990). In addition, the latter guides the implementation, and interpretation, for local-EM algorithms. For example, we expose a previously unknown connection between local-EM algorithms and penalized likelihood that is similar to the more familiar pairing of EM and likelihood. Examples include exploring the spatial structure of the disease Lupus in the City of Toronto. Supplemental materials for this article are available online.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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