A mass spectrometer‐based pore‐water sampling system for sandy sediments
Bibliographic record
Abstract
Abstract Highly permeable sandy sediments dominate the productive continental shelves and likely play a key role in global biogeochemical cycles. Despite their prominence, we currently have a poor understanding of how sandy sediments function and how they will respond to climate change. This knowledge gap is largely due to the difficulty in accurately sampling sandy sediments, as no method yet exists for measuring a range of analytes while accounting for advective pore‐water flow in these dynamic environments. To help address this, we developed a new pore‐water sampler that, when coupled to a portable mass spectrometer, can measure a suite of dissolved gases (e.g., O2, N2, Ar, CO2, and CH4) in sandy sediments. Here, we present a series of laboratory experiments to validate and calibrate the instrument, as well as proof‐of‐concept submersion and field tests. Our results show that with some design improvements, our system will be capable of sustained (hourly to diel) in situ measurements in sandy sediments. This new approach has the potential to provide a large volume of high‐quality data in any aquatic system with sandy sediments and thus greatly advance our understanding of biogeochemical processes occurring in these environments.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".