A method for a fast and economical in situ collection of pore water in sandy sediments
Bibliographic record
Abstract
Marine sediment represents one of the most significant carbon reservoirs. Extraction of pore water from the uppermost 10 cm of the sediment column, just below the sediment-water interface, in coastal environments can be used to assess the rapid chemical changes caused by extensive subsurface biological and chemical reactions. However, owing to relatively large grain size of the sediment in some shallow coastal environments, sediment and pore water mixes quickly making collection of core samples for pore water extraction extremely difficult. Here, we present a simple method utilizing Rhizons to collect in situ pore water from coastal environments. We conducted both ex situ and in situ experiments, using fluorescein as a tracer, to understand the influence of overlying water during pore water collection through this method. These experiments also allowed us to assess the volume of pore water collected before fluorescein is observed in the samples at different vertical depths within the sediment column. We found that: 1. discarding the initial 2 mL of water minimizes the memory effect, 2. collection of 10 mL of water avoids interference from surrounding pore water and the overlying water column and 3. a vertical resolution of minimum 2 cm could be attained for sediments with grain size of 1 mm and less. We finally present data from two linear transects from different sites in the Gulf of Aqaba. Results from these case studies demonstrate expected patterns of high concentrations of iron and hydrogen sulfide in pore water from the sediment where surface coloration suggested iron and sulfate reduction was dominating, respectively. Both case studies, therefore, independently validated the Rhizon sampler method for collecting pore water in situ from marine sediments in coastal 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".