In Situ Two-Dimensional High-Resolution Profiling of Sulfide in Sediment Interstitial Waters
Bibliographic record
Abstract
A recently developed technique of diffusive gradients in thin films (D6T)-computer-imaging densitometry (CID) was improved to study in situ two-dimensional distributions of sulfide in sediment interstitial waters adjacent to the DGT device. The in situ profiler accumulates sulfide from the sediment interstitial water through a diffusive gel onto a AgI binding gel to form black Ag2S. The amount of sulfide bound in the binding gel is then determined from the gray scale density of the dried binding gel. New gel-making procedures were employed to produce stable, evenly dispersed AgI binding gels and to minimize the darkening of the AgI gel upon exposure to light The improved DGT-CID technique was used to profile the distribution of sulfide in sediment interstitial waters at Delta Marsh, a highly productive Prairie wetland on the south shore of Lake Manitoba, at a vertical and lateral resolution of < or = 0.4 mm. The in situ high-resolution microprofiles revealed unprecedented two-dimensional heterogeneity in sulfide concentrations in the sediment interstitial waters adjacent to the DGT device. The mosaic distribution of oxic and sulfidic microenvironments suggested not only the complexity and heterogeneity of the biogeochemistry of sulfur species and sulfide-binding metals (e.g., Cd, Cu, Pb, Hg, Zn) in sediments but also the capability of aquatic organisms for coping with the sulfidic environment
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".