Bibliographic record
Abstract
Combined-accelerated stress testing (C-AST) simultaneously combines stress factors of the natural environment (including UV radiation, temperature, humidity, electrical current, and external mechanical force) into a single test that requires fewer modules, fewer chambers, and makes it possible to discover weaknesses in new designs that are not known a-priori. C-AST reduces risk, accelerates time to market, and improves bankability by reducing costly overdesign using test levels not exceeding those seen in the natural environment. This project seeks to evaluate strengths and weaknesses of modern cell interconnect designs with combined-accelerated stress testing (C-AST) to screen multiple climates along with finite element and failure analysis to determine the potential for a 50-year life. Supporting this, we seek to develop methods of characterization to assess the degradation of interconnects, enable predictive rate models with material forensics and FEA and show C-AST's ability to find interconnect failures and benchmark it relative to other accelerated stress test methods like temperature cycling and cyclic dynamic mechanical loading. Examples of modern interconnect designs covered are Canadian Solar Hetero Technology ribbon enabling closer cell interconnections, SmartWire (eg. Meyer-Berger technology), and shingled cells with new interconnect materials.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".