A new multisource and high‐frequency approach to measuring <i>δ</i><sup>2</sup>H and <i>δ</i><sup>18</sup>O in hydrological field studies
Bibliographic record
Abstract
Measurements of δ 2 H and δ 18 O in isotope‐based field studies have fundamentally improved our understanding of water flow and transport time scales in soils and headwater catchments. Until recently, however, technical constraints have limited the temporal resolution at which water samples could be collected and analyzed. We introduce a new sample acquisition system—consisting of a four‐channel peristaltic pump, custom flow manifold, and CTC LCPAL auto‐sampler—that is paired with a field‐deployable laser spectrometer (LGR LWIA). Our system enables high‐frequency (subhourly) measurement of δ 2 H and δ 18 O in as many as four water sources. We deployed the system at a field site in Corvallis, OR, USA, where we measured the δ 2 H and δ 18 O composition of precipitation and the drainage from two lysimeters. The system produced δ 2 H and δ 18 O time series for precipitation and drainage from each lysimeter at a temporal frequency of one sample every 34 min, which, on average, corresponded to 0.84, 0.63, and 0.48 mm of precipitation or lysimeter drainage per sample. The high‐frequency data showed substantially greater short‐term variability than observed when sampling at successively longer time intervals. The system and sampling configuration are versatile and can be adapted to sample multiple water flows at variable frequencies depending on the characteristic transit times of each source.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".