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
This paper aims to improve probability-based ranking (e.g. AUC) under decision-tree paradigm. We observe the fact that probability-based ranking is to sort samples in terms of their class probabilities. Therefore, ranking is a relative evaluation metric among those samples. This motivates us to use a lazy learner to explicitly yield a set of unique class probabilities for a testing sample based on its similarities to the training samples within its neighborhood. We embed lazy learners at the leaves of a decision tree to give class probability assignments. This results in the first model, named Lazy Distance-based Tree (LDTree). Then we further improve this model by continuing to grow the tree for the second time, and call the resulting model Eager Distance-based Tree (EDTree). In addition to the benefits of lazy learning, EDTree also takes advantage of the finer resolution of a large tree structure. We compare our models with C4.5, C4.4 and their variants in AUC on a large suite of UCI sample sets. The improvement shows that our method follows a new path that leads to better ranking performance.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.003 | 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