Bibliographic record
Abstract
One important problem in crowdsourcing is that of assigning tasks to workers. We consider a scenario where a worker is traveling on a preferred/typical path (e.g., from school to home) and there is a set of tasks available to be performed. Furthermore, we assume that: each task yields a positive reward, the worker has the skills necessary to perform all available tasks and he/she is willing to possibly deviate from his/her preferred path as long as he/she travels at most a total given distance/time. We call this problem the In-Route Task Selection (IRTS) problem and investigate it using the skyline paradigm in order to obtain the exact set of non-dominated solutions, i.e., good and diverse solutions yielding different combinations of smaller or larger rewards while traveling more or less. This is a practically relevant problem as it empowers the worker as he/she can decide, in real time, which tasks suit his/her needs and/or availability better. After showing that the IRTS problem is NP-hard, we propose an exact (but expensive) solution and a few others practical heuristic solutions. While the exact solution is suitable only for reasonably small IRTS instances, the heuristic solutions can produce solutions with good values of precision and recall for problems of realistic sizes within practical, in fact most often sub-second, query processing time.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".