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 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 | 0.119 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.031 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".