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
completion model. There are N-cities (N=1, 2, 3….n) and J jobs (1, 2,…q). The distance between each pair of cities is mentioned and is denoted by dij. For each city seasonal jobs have been mentioned for the agents whom they have to perform them in two seasons/periods. In the distance matrix the distance between each pair of the cities is symmetric. They have to do m jobs (truncated,i.e m<q) the agents starts from Head Quarter City and returns to it in the same path while completing all the m in both season jobs. The agents perform the first seasonal jobs at the cities when they move from Head Quarter City to the cities and they perform second seasonal jobs while return in the same path to the Head Quarter city. The aim of the problem is to find the paths of the agents such that the total distance travelled by them is minimum while completing all the m jobs. We consider a suitable numerical example and solved the problem by lexicographic search approach using pattern recognition technique. Index Terms- Integer programming, Lexi-Search approach using Pattern recognition technique, Symmetric distance. T I.
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) | 0.011 | 0.002 |
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