SQUEAKY CLEAN CELLULOSE: COMPARING PRETREATMENT EFFECTIVENESS ON SINGLE TREE RINGS AND WOODEN LATHS
Bibliographic record
Abstract
ABSTRACT Obtaining accurate radiocarbon ( 14 C) results from wood samples requires the extraction of cellulose. In the past, this has been done using different combinations of methods including acidified bleaching, acid-base-acid, and strong bases. This often becomes a time-consuming task in most analyses, especially when single ring isotope chronologies are needed from wood samples. Using 14 C and Fourier-transform infrared spectroscopy (FTIR), we tested four different pretreatment methods to determine their ability to produce high quality cellulose. We then adjusted the best methods to determine a method for pretreating wood laths of multiple rings. A sequence of base-acid-base-acid-bleach + strong base (BABAB+) and BABAB produced the most accurate results when compared to the Bomb20 Northern Hemisphere Zone 1 curve with an average difference in Δ 14 C of 1.3‰ and 5.8‰ respectively. These methods were adjusted to pretreat an entire wood lath and a comparison of the FTIR results suggest that our adjusted BABAB+ and BABAB produced high quality cellulose comparable to that of an individually pretreated ring. The possibility to pretreat wood lath samples has the potential to more than double the number of tree rings which can be pretreated in a week. This is a significant reduction in time when creating long tree ring chronologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".