Life Cycle Impact Assessment\nof Terrestrial Acidification:\nModeling Spatially Explicit Soil Sensitivity at the Global Scale
Bibliographic record
Abstract
This paper presents a novel life cycle impact assessment\n(LCIA)\napproach to derive spatially explicit soil sensitivity indicators\nfor terrestrial acidification. This global approach is compatible\nwith a subsequent damage assessment, making it possible to consistently\nlink the developed midpoint indicators with a later endpoint assessment\nalong the cause-effect chaina prerequisite in LCIA. Four different\nsoil chemical indicators were preselected to evaluate sensitivity\nfactors (SFs) for regional receiving environments at the global scale,\nnamely the base cations to aluminum ratio, aluminum to calcium ratio,\npH, and aluminum concentration. These chemical indicators were assessed\nusing the PROFILE geochemical steady-state soil model and a global\ndata set of regional soil parameters developed specifically for this\nstudy. Results showed that the most sensitive regions (i.e., where\nSF is maximized) are in Canada, northern Europe, the Amazon, central\nAfrica, and East and Southeast Asia. However, the approach is not\nbereft of uncertainty. Indeed, a Monte Carlo analysis showed that\ninput parameter variability may induce SF variations of up to over\n6 orders of magnitude for certain chemical indicators. These findings\nimprove current practices and enable the development of regional characterization\nmodels to assess regional life cycle inventories in a global economy.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".