Tree-Structured Task Allocation for Audit Team With Sequential and Parallel Execution via E-CARGO Model
Bibliographic record
Abstract
Task allocation is a critical aspect of teamwork, especially in complex projects such as auditing. In audit task allocation, collaboration among team members is essential, as the success of the project depends not only on individual expertise but also on efficient teamwork. This process is closely related to the GMRA problem, a complex optimization challenge. This study tackles the challenges of task allocation in phased tasks and multitasking within auditing. We propose a sequential and parallel assignment method based on tree-structured tasks, formalized as the TSTA<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sp</sub> problem. Through both theoretical and experimental analyses, we demonstrate its feasibility and effectiveness. Additionally, we introduce improvements using the Gurobi solver and validate their impact. The results show significant optimization in audit task allocation.
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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 | 0.000 | 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 | 0.001 | 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 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".