Cashing in on the Ring of Fire: Its "value" to Ontario's EV Market & Supply Chain, A Life Cycle assessment of the Mining Sector
Bibliographic record
Abstract
<p>The climate is changing, largely in part due to anthropogenic behaviour — the use of fossil fuels. The paper explores one of the largest contributors to global carbon emissions: the transportation sector and the shift towards electrification. However, rather than the more typical approach taken to directly evaluate the transportation sector, this paper opted for a rather unique approach by beginning the evaluation of emissions from the mining sector, where the commodities for electric vehicles are largely sourced. After evaluating two interdependent sectors' emissions using a life cycle assessment approach, the results indicated a net benefit for the mining project in question in Ontario, and more broadly the benefit of bringing an electric vehicle supply chain to Ontario. On the global stage, diversifying the commodities needed for transportation -- a premise considered vital to individual and national economic prosperity -- is necessary to bring about more balanced global stage. If projects similar to those evaluated in this study were done with forethought for the environment, a greener future might just be possible.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".