Bibliographic record
Abstract
In risk assessment, environmental and human health protection is informed by scientific knowledge of hazard. For earth materials, including bedrock and its overlying mantle of unconsolidated mineral particulate, risk for geochemical hazard is based on element concentrations - numbers, established in biological testing. In showing that hazard potential varies with mineralogy, and that mineral composition varies among geological terranes, geoscience shows that no single element concentration can establish a universal measure of acceptable risk in earth materials. Risk assessment requires knowledge of sample grain size and mineral partitioning among grain size fractions, as well as of the wet chemical digestion used for analyses. In showing how geology affects both the measure of risk and its interpretation, geoscience also shows that regulatory approaches must evolve to accommodate the natural variability that is an inherent characteristic of earth materials. As natural geochemical background variation - the reference level for industrial liabilities, originates in mineralogy, itsvariation may be simplified in terms of geological provenance, process, and past. For unweathered earth materials, geological maps and models establish a stable and deterministic reference framework for ecological hazard potential. With increase in weathering and soil formation, however, there is increasing need incorporate other natural sciences, including pedology and biology, in risk assessment.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.141 | 0.063 |
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".