Learning and forgetting effects on maintenance outsourcing
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 article studies the effects of learning and forgetting on the design of maintenance outsourcing contracts. Consider a situation in which a manufacturer offers an outsourcing contract to an external contractor to maintain a manufacturing process. Under the contract, the contractor schedules and performs preventive maintenance and repairs the process whenever a breakdown occurs. Two types of learning effects on the cost and time of performing preventive maintenance are considered: learning from experience (natural) and learning by a costly effort/investment. It is assumed that forgetting occurs under each learning type. A model is developed for designing an optimal outsourcing contract to maximize the manufacturer's profit. An extensive numerical analysis is carried out to empirically demonstrate the effects of learning and forgetting on the optimal maintenance contract and the manufacturer's profit.
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) | 0.000 | 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