Bibliographic record
Abstract
Closure in geochemical data serves to complicate interpretation because it adds variation unrelated to geochemical processes. Geoscientists have used three methods to avoid closure: the Theorem of Geochemical Material Transfer, molar element ratio analysis and compositional data analysis. Whereas each has its advantages, in many scenarios, mathematically induced closure effects are modest and/or overwhelmed by the effects of material transfer, preventing closure from impairing conclusions derived using geochemical data analysis. This paper derives an equation from the definition of a concentration illustrating that the relative concentration change is a function of the relative change in the amount of the element (material transfer) and the relative change in the size of the rock (closure): d x/x = d X/X – d S/S , where x is component concentration, X is the amount of that component in a rock and S is the size of the rock. Functional analysis of this equation identifies two scenarios where closure does not add significant variance or distortion to concentration data: (1) in perfect exchange processes (when the system size doesn't change, so closure effects are absent (d S = 0)); and (2) when the relative amount of material transfer is larger than the relative change in system size (d X/X >> d S/S ). This latter scenario occurs when X is small (the concentration is at minor/trace levels) or when d X is large (the amount of material transfer is large relative to S ). In each scenario, the effect of material transfer (d X/X ) is larger than that of closure (d S/S ), making geochemical data easy to interpret.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".