The Fe/Na ratio, a framework for modelling trace element distributions in Jamaican soils
Bibliographic record
Abstract
The trace element geochemistry of Jamaican soils (<150 μm) is extremely variable due to their development on diverse parent materials, varying maturity (ages range from recent to >5 Ma) and the variety of soil-forming processes. It is demonstrated that the Fe/Na ratio of the soils forms a useful quantitative framework for studying trace element distributions. The ratio varies 2.5 orders of magnitude from <2 in newly developed inceptisols to >500 in oxisols and terra rossas that have been developing for >5 Ma. The form of the trace element distributions with respect to Fe/Na ratio may be interpreted in the context of mineralogy of the parent material, removal of labile trace element fractions during soil development, sequestration in stable secondary forms, and ultimate concentration of stable primary resistate and secondary minerals by terra rossa soil formation processes that lead to elevated Fe and Al levels and the depletion of silica and base cations, Ca 2+ , Mg 2+ , Na + and K + . As examples, the distributions of U and As are modelled as functions of the Fe/Na ratio. Use of robust regression procedures identifies individual soil samples that exhibit divergent patterns of trace element concentration. Some of these can be linked to local bedrock sources, while others are more likely related to exotic volcanic ash-fall material from Central America deposited on proto-Jamaica in the late Miocene.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".