High-Throughput BAC Fingerprinting
Bibliographic record
Abstract
This chapter describes a nonradioactive, agarose gel-based, high-throughput DNA restriction digest fingerprinting methodology first described by Marra et al. ( 1 ) for use in the construction of high-resolution physical maps from low-copynumber, large-insert clones. The procedure is robust and allows for the recovery of clone insert size information. Initially used to construct sequence tag site (STS)-based contigs ( 1 ), the methodology has also been applied to whole-genome, random-clone strategies that have resulted in the construction of high-resolution, sequence-ready physical maps of the genomes of Arabidopsis thaliana ( 2 , 3 ), human ( 4 , 5 ), Caenorhabditis briggsae ( 6 ), and Cryptococcus neoformans ( 7 ). The methodology is currently being employed in the construction of physical maps for several other large, mammalian genomes, such as those of mouse ( 8 ), rat and bovine. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".