A novel approach for the homogenization of cellulose to use micro‐amounts for stable isotope analyses
Bibliographic record
Abstract
Climate reconstructions using stable isotopes from tree-rings are steadily increasing. The investigations concentrate mostly on cellulose due to its high stability. In recent years the available amount of cellulose has steadily decreased, mainly because micro-structures of plant material have had to be analyzed. Today, the amounts of cellulose being studied are frequently in the milligram and often in the microgram range. Consequently, homogeneity problems with regard to the stable isotopes of carbon and oxygen from cellulose have occurred and these have called for new methods in the preparation of cellulose for reliable isotope analyses. Three different methods were tested for preparing isotopically homogenous cellulose, namely mechanical grinding, freezing by liquid nitrogen with subsequent milling and ultrasonic breaking of cellulose fibres. The best precision of isotope data was achieved by freeze-milling and ultrasonic breaking. However, equipment for freeze-milling is expensive and the procedure is labour-intensive. Mechanical grinding resulted in a rather high loss of material and it is also labour-intensive. The use of ultrasound for breaking cellulose fibres proved to be the best method in terms of rapidity of sample throughput, avoidance of sample loss, precision of isotope results, ease of handling, and cost.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".