Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract—In biological sequence research, the positional weight matrix (PWM) is often used to search for putative transcription factor binding sites. A set of experimentally verified oligonucleotides known to be functional motifs are collected and aligned. The frequency of each nucleotide A, C, G, or T at each column of the alignment is calculated in the matrix. Once a PWM is constructed, it can be used to search from a nucleotide sequence for subsequences that can possibly perform the same function. The match between a subsequence and a PWM is usually described by a score function, which measures the closeness of the subsequence to the PWM as compared with the given background. Nevertheless, the score function is usually motif-length-dependent and thus there is no universally applicable threshold. In this paper, we propose an alternative scoring index (G) varying from zero, where the subsequence is not much different from the background, to one, where the subsequence fits best to the PWM. We also propose a measure evaluating the statistical expectation at each G index. We investigated the PWMs from the TRANSFAC and found that the statistical expectation is significantly (p<0.0001) correlated with both the length of the PWMs and the threshold G value. We applied this method to two PWMs (GCN4_C and ROX1_Q6) of yeast transcription factor binding sites and two PWMs (HIC1-02, HIC1_03) of the human tumor suppressor (HIC-1) binding sites from the TRANSFAC database. Finally, our method compares favorably with the broadly used Match method. The results indicate that our method is more flexible and can provide better confidence.
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.
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 it