Bibliographic record
Abstract
Sustainment is the most important part of the aircraft life cycle. After a production program ends there are decades of work to support the aircraft that are flying in the fleet. The average aircraft age of the B-52 is 55 years old as of today. With a decommission estimate of 2040, the fleet's age could hit 90 years of sustainment of a given aircraft. These aircraft require both planned and unplanned maintenance, which requires the original equipment manufacturer (OEM) to supply spare parts. When the required spare parts are not available, it can result in aircraft on ground (AOG) events and missions unable to be flown. During production, an OEM's suppliers have a steady cadence of part orders which results in a steady flow of parts through their facility. Over the lifecycle of sustainment, part orders are more likely to come in smaller quantities and at unpredictable intervals. This results in suppliers needing to start and stop their production lines for these parts, or the need to inventory parts, which creates several challenges. It is not easy to restart a production line. For some parts there is still the element of tacit knowledge that is essential to manufacturing the parts. If there is a break in production, the tacit knowledge can be lost causing a long process to restart production, resulting in increased scrap and longer than normal lead times. Breaks in the production flow also result in suppliers focusing their resources on other projects, so capacity is not guaranteed when a sustainment order is needed. Further, because many sustainment parts were designed decades ago using manufacturing processes that were most robust during that time period, many advanced technologies for manufacturing are not applied to sustainment parts. As time goes on, parts with multiple sources become parts with sole sources or worse, they become obsolete. Options for procurement become limited, often requiring protracted negotiations and requiring the OEM to accept long lead times, unit cost increases, requests to reimburse the supplier for non-recurring expenses (NRE) to re-start the line, and/or large minimum buy quantities. If parts have become obsolete, then efforts are typically initiated to qualify a new supplier to build the parts as originally designed or to qualify a replacement part that is very similar to the original. This can be an effective approach for some parts and suppliers, but it is a time consuming and costly strategy that can still be ineffective in the end. Using the advanced technologies that have been developed since their original design are a much more effective, responsive, and flexible approach to addressing these supply challenges.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.016 |
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".