Bibliographic record
Abstract
ABSTRACT In April and August 0f 2009, seventeen (17) industry subject-matter-experts were assembled in Philadelphia, PA and charged with executing two tasks: 1.) Benchmark the knowledge base of Pb-free technology and its associated impact on performance in aerospace and defense (A&D) products (Phase 1) and 2.) Develop a roadmap that would close the knowledge gaps providing solutions and/or mitigation strategies to confidently address all associated risks (Phase 2). This entire effort was named “The Lead- Free Manhattan Project” (LFMP). The results from both phases were reported in two separate documents with a combined page count of five hundred seventy-nine (579) pages. The page count is reported here not to impress the reader but to emphasize the fact that assessing the impact of any major material change on an industry has far-reaching effects in every aspect of product life cycle, i.e. concept, design, reliability, supply chain, assembly, test, and logistics. Subsequently, as the cost to execute the project was, understandably high and in response to several industry and government inquiries, a special task team, sponsored by the IPC Pb-free Electronics Risk Mitigation (PERM) Council, was stood up in 2012 with a charter to identify priority research areas to help close the knowledge gaps regarding impact and risks associated with the implementation of Pbfree materials in high-performance, i.e. A&D, electronics. The priorities were based on the output of the LFMP. The white paper was released in February 2014. An applicationbased strategy was employed and, as a result, five platform categories were identified in assessing priorities. Those platforms were avionics, ground-based systems, missiles, space systems, and submarine-sea systems. A panel of platform subject-matter-experts, from within the PERM Council, was stood up and their subsequent review of performance and service conditions disclosed that the following four areas should be given attention based on their significant influence and control by service and environmental conditions (in addition to supply chain practices which are beyond the scope of this paper): Tin whisker failure modes Tin whisker risk mitigation Complex systems logistics Pb-free interconnections (e.g. solder joints) including reliability models, effects and assembly qualification Despite this prioritization, some preliminary effort would be required to re-plan the tasks as these were part of an integrated planning approach used in the original LFMP. Subsequently, the impact of performing these as stand-alone tasks would need to be assessed. This paper will briefly re-visit the “mission” of the Lead- Free Manhattan Project and provide some detail on the generation of this prioritized list.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".