A universal percussion corer for sampling lake sediments
Bibliographic record
Abstract
Percussion coring systems rely on a moveable weight to push a core barrel into the sediment. The corer, attached to a cable, is lowered through the water column until it reaches the sediment. A second line is then used to raise then drop the weight on top of the core barrel. Over the years, several types of lightweight, percussion coring systems have been developed for use in remote locations where the weight of the equipment is a major concern. We used a Universal percussion corer to obtain sediment from lakes near the Jorge Montt Glacier in the Chilean Patagonia. Choosing a core sampling system was complicated by logistics: the remoteness of the field site and by the cumbersome nature of the equipment (including two small, inflatable boats) which had to be carried, on foot, over difficult terrain. We chose this particular corer as it is relatively inexpensive, lightweight, and easily assembled. It consists of a Universal core head (69 mm diam.), gravity weights, clear polycarbonate core barrels (120 cm and 240 cm long) and a slide hammer. We worked on shallow lakes (less than 5 m depth) from an unstable platform. However, this corer can be deployed in much deeper lakes. We could only recover short sediment cores because the lakes around Jorge Montt Glacier are very young. Nonetheless, we found this percussion core sampling system to perform well in the field but its full potential could not be assessed due to various particulars of the expedition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.005 |
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".