Improving process granularity of life cycle inventories for battery grade nickel
Bibliographic record
Abstract
Batteries are essential to transition to a fossil-free energy system, but only if coherently planned will their manufacturing generate minimal environmental impacts. Aggregated life cycle inventories for battery-grade nickel prevent life cycle analysts from easily pinpointing key impact contributors. The present work reconstructs inventories via disaggregation of current and emerging processing routes. Improving process granularity demonstrates variability in climate impacts of 74 kgCO 2eq /kWh for nickel sourcing of an NMC-811 cell. Furthermore, the global ecoinvent v.3.9.1 dataset for nickel sulfate could gravely underestimate climate impacts by 120 kgCO 2eq /kg Ni equivalent. Major contributors to climate impacts are readily identified for six nickel processing pathways, spanning two mineral families – laterite and sulfide – and three main processing routes – hydrometallurgy, bioleaching and pyrometallurgy. A preliminary assessment of all impact categories highlights the need for both improved fate models and data collection on inventory parts such as tailings management which are often neglected in carbon-focused studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".