Material and Digital Reconstruction of Fragmentary Dead Sea Scrolls
Bibliographic record
Abstract
Scaling of PAM plates in relation to features of their rulers 47 Margin of error for the reconstruction of all lines in 1QIsaa 97 Margin of error for the reconstruction of lines with no vacats in 1QIsaa 98 Margin of error for the reconstruction of column height in 1QIsaa 99 Margin of error for the reconstruction of several consecutive columns in 1QIsaa -version 3.A 100 Margin of error for reconstruction of several consecutive columns in 1QIsaastage 3.B 101 Margin of error for the reconstruction of all lines in 1QS columns 8-11 102 Margin of error for the reconstruction of lines with no vacats in 1QS columns 8-11 103 Margin of error for the reconstruction of column height in 1QS 104 Margin of error for the reconstruction of several consecutive columns in 1QSversion 3.A 106 Margin of error for the reconstruction of several consecutive columns in 1QSversion 3.B 106 Margin of error for the reconstruction of all lines in 11Q5 Columns 20-24 108 Margin of error for the reconstruction of lines with no vacats in 11Q5 columns 20-24 108 Margin of error for the reconstruction of column height -11Q5 109 Projecting the number of missing lines from one copy to the other 142 Projecting the number of missing lines from 4Q418a to 4Q418 144 Positions of fragments to the left 229 Positions of fragments to the right 231 Columns and margins of 4Q418a 232 Comparison of missing text at the beginning of 4Q418a and 4Q417 246 Number of columns per sheet in 4Q418a 247
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".