The Effects of Possible Contamination on the Radiocarbon Dating of the Dead Sea Scrolls II: Empirical Methods to Remove Castor Oil and Suggestions for Redating
Bibliographic record
Abstract
While kept at the Rockefeller Museum in East Jerusalem, many Dead Sea Scroll fragments were exposed to castor oil by the original team of editors in the course of cleaning the parchments. Castor oil must be regarded as a serious contaminant in relation to radiocarbon dating. If modern castor oil is present and is not removed prior to dating, the 14C dates will be skewed artificially towards modern values. In Rasmussen et al. (2001), it was shown that the standard AAA pretreatment procedure used in the 2 previous studies dating Dead Sea Scroll samples (Bonani et al. 1992; Jull et al. 1995) is not capable of removing castor oil from parchment samples. In the present work, we show that it is unlikely that castor oil reacts with the amino acids of the parchment proteins, a finding which leaves open the possibility of devising a cleaning method that can effectively remove castor oil. We then present 3 different pretreatment protocols designed to effectively remove castor oil from parchment samples. These involve 3 different cleaning techniques: extraction with supercritical CO2, ultrasound cleaning, and Soxhlet extraction—each with their own advantages and disadvantages. Our data show that the protocol involving Soxhlet extraction is the best suited for the purpose of decontaminating the Dead Sea Scrolls, and we recommend that this protocol be used in further attempts to 14C date the Dead Sea Scrolls. If such an attempt is decided on by the proper authorities, we propose a list of Scroll texts, which we suggest be redated in order to validate the 14C dates done earlier by Bonani et al. (1992) and Jull et al. (1995).
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".