Geochemical and isotopic (O-Fe-Sr-Nd) characterization of reference materials relevant to environmental impact assessments
Bibliographic record
Abstract
This study presents multi-faceted characterization of riverine and marine reference materials (RM) relevant for undertaking environmental impact assessments associated with mining or anthropogenic activities. These include composite stream sediments (JSd-2, JSd-3), marine sediments (MESS-3, HISS-1), a banded iron formation (FeR-4) and a basalt (BHVO-2). Whole rock major and volatile element contents (C, H, S) contents were determined using X-ray fluorescence and an elemental analyzer, respectively. Following hotplate digestions 47 trace elements were determined via solution induction coupled plasma mass spectrometer (ICP-MS). Oxygen isotope compositions (δ18O) were measured using an isotope ratio-MS. Stable Fe (δ56Fe) and radiogenic 87Sr/86Sr and 143Nd/144Nd isotope compositions were measured using multi-collector ICP-MS. These results demonstrate that caution should be applied when selecting a sedimentary RM given some suffer from significant heterogeneity (e.g. HISS-1) across multiple parameters including volatile and trace element contents and stable and radiogenic isotope compositions. Due to the potentially diverse source components of siliciclastic sediments (i.e. inherited heterogeneity), when conducting environmental impact assessments across certain settings (e.g., riverine; estuarine; marine), a wider uncertainty window should be applied before definitively ascribing subtle differences to exogenous contamination.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".