High Resolution Pore Water δ<sup>2</sup>H and δ<sup>18</sup>O Measurements by H<sub>2</sub>O<sub>(liquid)</sub>−H<sub>2</sub>O<sub>(vapor)</sub> Equilibration Laser Spectroscopy
Bibliographic record
Abstract
A new H2O(liquid)-H2O(vapor) pore water equilibration and laser spectroscopy method provides a fast way to obtain accurate high resolution deltaD and delta18O profiles from single core samples from saturated and unsaturated geologic media. The precision and accuracy of the H2O(liquid)-H2O(vapor) equilibration method was comparable to or better than conventional IRMS-based methods, and it can be conducted on geologic cores that contain volumetric water contents as low as 5%. Significant advantages of the H2O(liquid)-H2O(vapor) pore water equilibration method and laser isotopic analysis method include dual hydrogen- and oxygen-isotope assays on single small core samples, low consumable and instrumentation costs, and the potential for field-based hydrogeologic profiling. A single core is sufficient to obtain detailed vertical isotopic depth profiles in geologic, soil, and lacustrine pore water, dramatically reducing the cost of obtaining pore water by conventional wells or physical water extraction methods. In addition, other inherent problems like contamination of wells by leakage and drilling fluids can be eliminated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".