Bibliographic record
Abstract
The billionaire recently lambasted iPhone producer, Apple Inc., for sending parts of its supply chain overseas. actually think we can say now, and I really believe this, we're gonna get things coming (...) we're gonna get Apple to start building their damn computers and things in this country, instead of in other countries, he was quoted as bellowing at a rally earlier this year. Waiting in the wings is Canada. As the larger of the US' two direct neighbours in terms of land mass and with a well-developed mining industry and progressive exploration sector, Canada is in an advantageous position when it comes to supplying the US with freshly dug minerals and metals. Apart from mining these in its own soil, a significant proportion of the projects underway within US borders are being operated by Canadian companies, most of which are listed on the Toronto Venture Exchange (TSX-V). Meanwhile, graphite, the Cinderella of the lithium-ion (Li-ion) battery industry, looks likely to have another rough year this year, as demand from its traditional market - refractories for the steel industry - shrinks faster than consumption by green energy applications expands. Shruti Salwan, IM Analyst, outlines these headwinds but argues that demand for the battery raw material, spherical graphite, will spearhead the recovery in demand in 2016, if not prices ( p19 ).
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.191 | 0.081 |
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".