Bibliographic record
Abstract
MR. SMITH: Good afternoon, everyone.Thank you for the opportunity to be here.My research vice president, Sean McAlinden, intended to be here, 6 but unfortunately Sean truly fell off his horse last weekend.The good news is he is okay, so we can joke about it.The bad news is I have spent this week going all over the Midwest and covering for Sean.I have gone through the industry, giving a background of just about every type of discussion you could have on the issues related to the industry.I am hoping to give you some of that background.To give you a little bit of background about our organization, the Center for Automotive Research, as has been suggested, is a spinout from the University of Michigan.7 We spun out from the University of Michigan back in 2000.We are a not-for-profit organization.8 We still do, basically, the same work we did at the University.At the University, we were too much like consultants for the academics.Now, in the not-for-profit world, we are too academic for the consultants, so we have had to find a niche in between the two of them.The Center for Automotive Research focuses on a wide variety of topics in the automotive industry.We really pride ourselves on being a broad-based research group, funded by government (most of the time).Much of our financing is government focused.9 However, we get other foundation and other government work. .Brett Smith joined the Center for Automotive Research in 2000 after twelve years at the University of Michigan's Office for the Study of Automotive Transportation (OSAT).Over the past twenty years, Mr. Smith has divided his research efforts between automotive industry analysis, and advanced powertrain technology and strategy.He is currently leading several research efforts on advanced powertrain technology, including such projects on energy storage policy and plug in electric vehicle infrastructure issues.He is also program director for the Business of Plugging In, a conference held annually in Detroit, Michigan.Mr. Smith has authored numerous reports ranging from materials for education and training to aftermarket strategies.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".