Online In-Route Task Selection in Spatial Crowdsourcing
Bibliographic record
Abstract
Consider the following scenario: (a) a worker traveling on the shortest path between two locations in a city's road network, (b) he/she is willing to deviate from such path in order to complete tasks in the network, (c) tasks are associated with rewards and appear and disappear dynamically, i.e., they are not known in advance, and (d) the worker specifies a time budget which limits the total time he/she is willing to spend on his/her trip. Now assume the worker wants to minimize the detour from the original path while, at the same time, maximizing the rewards collected by completing tasks; clearly two competing criteria. We call this problem the Online In-Route Task Selection (Online-IRTS) query, and we investigate it using the paradigm of skyline queries in order to systematically explore different trade-offs between earned rewards and path deviation. Because of the online nature of the problem, i.e., irrevocable decisions about which task to perform have to be made without knowledge of future tasks, it is not possible to guarantee optimal solutions for the Online-IRTS query. Therefore, we propose two heuristic approaches, one is based on local optimizations, and the other one is based on incremental solutions, along with a method to evaluate the quality of their solutions w.r.t. the optimal offline solution. Our experiments using city-scale realistic datasets show that the first approach is more effective whereas the second is more efficient, allowing one to choose which approach to use according to his/her priorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".