Freeze Shoe Sampler for the Collection of Hyporheic Zone Sediments and Porewater
Bibliographic record
Abstract
The Starr and Ingleton (1992) drive point piston sampler (DPPS) design was modified by fitting it with a Murphy and Herkelrath (1996) type sample-freezing drive shoe (SFDS), which uses liquid carbon dioxide as a cryogen. Liquid carbon dioxide was used to freeze sediments in the lower 0.1 m of the core and the drive-point piston sealed the core at the top preserving the reductive-oxidation (redox) sensitive sediments from the atmosphere and maintaining natural stratigraphy. The use of nitrogen gas to provide positive pressure on the gas system blocked the ingress of water which froze on contact with the cryogen thus blocking the gas lines with ice. With this adaptation to the gas system cores could be collected at greater depths beneath the static water level. This tool was used to collect intact saturated sediment cores from the hyporheic zone of the tidally influenced Fraser River in Vancouver, British Columbia, Canada where steep geochemical and microbial gradients develop within the interface between discharging anaerobic groundwater and recharging aerobic river water. In total, 25 cores driven through a 1.5 m sampling interval were collected from the river bed with a mean core recovery of 75%. The ability to deploy this method from a fishing vessel makes the tool more cost effective than traditional marine-based drilling operations which often use barges, tug boats, and drilling rigs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.012 | 0.004 |
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".