Shk-9: A new tool in approach of glycoprotein annotation
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
Changes in glycosylation are involved in different human diseases, including cancer. The recognition of glycan-based biomarkers became one of the most strategic research areas. Scientists all over the world performed comprehensive screens and found a number of substances that can be matched to human cancer. Unfortunately, this data is difficult to access and utilize. Besides the advantage of a wide variety of available hardware, the diversity may software-wise complicate the data annotation. The growing databases provide the opportunity for more dependable templates, but are also a challenge for the execution of automatic protocols. To refine utilizing of these findings and contribute to scientific meta-analyses, we developed the ShK-9. Program outputs include lines of text containing a string found in the supplied list. These imprints are written into text files that can be imported into spreadsheet standard office programs for further analyses. The aim of this article is to introduce a new open source tool for working with data sets, called ShK-9. Keywords: Glycoprotein, Database, Linear search
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