Radiocarbon analysis of RNA, DIC, DOC and CH4 to constrain the sustainability of pumping Pleistocene aquifers in Bangladesh
Bibliographic record
Abstract
Pleistocene aquifers are key for lowering the chronic exposure of the rural population to arsenic. Too little is known, however, about the sources of reactive carbon that maintain reducing conditions in these low-arsenic aquifers. This matters as enhanced supply of reactive carbon due to perturbations in groundwater flow could potentially release arsenic to groundwater. To shed light on this process, our team measures the radiocarbon content of labile microbial matter (RNA) and compares it to the radiocarbon content of potential sources of reactive carbon. Results to date suggest that recent recharge supplies reactive carbon in the dissolved form rather than the sediment, possibly in part in the form of methane. This dynamic situation suggests arsenic concentration could vary of time and should be monitored in vulnerable areas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".