Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach
Bibliographic record
Abstract
A novel method of estimating the silica (SiO 2 ) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to first calculate the minimum SiO 2 , water (H 2 O) and carbon dioxide (CO 2 ) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO 2 . In a recursive step, a ‘consensus’ SiO 2 is then established as the average between the two aforementioned SiO 2 estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S and Ti elemental concentrations + ‘consensus’ SiO 2 + reported trace element concentrations converted to wt% + ‘normative’ H 2 O + ‘normative’ CO 2 ) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H 2 O and CO 2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO 2 is linear, strong ( R 2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO 2 concentrations with a sparser, publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO 2 values measured on the ground over the conterminous USA. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO 2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to always validate results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".