Antibody Characterization Report for Casein kinase II subunit alpha (CSNK2A1)
Bibliographic record
Abstract
A peer-reviewed antibody characterization article corresponding to this Zenodo preprint is openly available at F1000Research: https://doi.org/10.12688/f1000research.153243.2 This report presents a guide to selecting high-quality commercial antibodies against Casein kinase II subunit alpha (CSNK2A1) by Western blot, immunoprecipitation and immunofluorescence, using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. This study was funded in part by the Simons Foundation Autism Research Initiative (SFARI), an organization that funds innovative research to enhance the understanding, diagnosis and treatment of autism spectrum disorders.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Antibody characterization report benchmarking commercial antibodies against CSNK2A1 with knockout-controlled protocols; the closest rubric anchor (Western blot protocol reproducibility) puts reagent-level validation OUT, yet such standardized characterization campaigns are explicitly reagent-reproducibility infrastructure, so this sits on the boundary.
The study evaluates antibody performance in laboratory assays rather than research practice itself.
Lab reagent antibody characterization and assay performance; polysemy of validation, not study of research practice.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.049 |
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".