Bibliographic record
Abstract
It limits the relief available to litigants against prospective licencees, forcing them to have some skin in the game and bringing the process closer to the British loser pays system of Canada and Australia, where you need to be pretty sure in order to file a case, [Laura Skaer] said. Where will the rare earths permanent magnets come from to enable this truly 'run silent, run deep' technology? he asked. What are the odds that China will generously step up and give America a 'good deal' on magnet elements? Wouldn't domestic sourcing through a complete home-grown supply chain be more sensible and secure? Not one of the witnesses during our House Natural Resources Committee hearing could name a single that would not be considered a strategic and critical mineral under the all-inclusive definition in this bill, Rep. Alan Lowenthal, a Democratic congressman for California, told IM. Sand, gravel, and every other we know of would qualify as a strategic and critical mineral and receive environmental review short-cuts, he added.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".