Expressing biomarker data in stoichiometric terms: shifts in distribution and biogeochemical interpretation
Bibliographic record
Abstract
Quantitative biomarker analysis is an invaluable tool used routinely by organic geochemists to interpret and explain environmental processes. Since the advent of organic geochemistry, all levels of biomarker methodology from wet chemistry to data interpretation have significantly advanced; however, an important aspect of data analysis has remained constant and that is the expression of biomarkers in terms of mass (e.g., mg gOC−1). We argue that biomarkers are more appropriately expressed in terms of moles (e.g., mmol molOC−1) to better reflect molecular‐level distribution of compounds of interest, as well as introducing a chemical consistency for better comparability and transferability between data sets. Using modeled and real data culled from the literature, we demonstrate that the use of moles is not a trivial conversion and that distribution and relative weighting of biomarker data sets are shifted and affect the total data structure. The shift is sometimes strong enough to exert changes in interpretation (e.g., microbial abundance in an estuarine system), alter proxies (e.g., terrestrial‐to‐aquatic ratio), and potentially influence the use of multivariate statistical methods (e.g., principal components analysis).
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".