Sampling, handling, and preparation of peat cores from bogs: review of recent progress and perspectives for trace element research
Bibliographic record
Abstract
Peat bogs are valuable archives of environmental change, including climate history, landscape evolution, and atmospheric deposition of trace elements, fallout radionuclides, and organic contaminants. Maintaining the fidelity of peat samples during collection and handling can be challenging, given that bogs consist mainly of fossil plant materials that typically have a very low density and are easily compressed. The surface layers of bogs, which are dominated by living plants and poorly decomposed fibrous peats, are especially problematic. To extract peat monoliths, we use a Belarus corer for deep layers and a Wardenaar device for surface layers. Both corers are constructed using titanium alloys to improve strength, reduce weight, and minimize the risk of contamination by the trace metals of environmental relevance. In this review, we include detailed drawings of the Belarus corer and photographs of the modifications to the Wardenaar corer. Modifications to the motorized Noernberg corer for frozen peat are described, and a complete set of drawings provided. A summary is given of simple procedures to minimize the risk of metal contamination in the laboratory from slicing and subsampling the peat cores and milling the dried samples.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".