Simple Method for Preparation of Fluor/Hapten-Labeled dUTP
Bibliographic record
Abstract
Many projects, such as multiplex-fluorescence in situ hybridization (M-FISH) karyotyping, require the use of relatively large amounts of multiple fluor- or hapten-labeled nucleotides for the preparation of DNA probes. Such a requirement makes these experimental approaches prohibitively expensive for many researchers. The cost of such nucleotides can be reduced approximately 99% by purchasing the chemical precursors, fluor or hapten succinimidyl esters and 5-(3-aminoallyl)-2'-deoxyuridine 5' triphosphate (AA-dUTP), and performing the simple coupling/purification described here. It is possible to finish four to ten different fluor/hapten dUTP preparations of 2.5 microM scale within a 24 h period. The reagent cost for each preparation ranges from $33-$237 per microM, depending on the fluor/hapten. This laboratory uses such nucleotide preparations to prepare FISH probes by nick translation or PCR amplification.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".