Legacy Submission
Bibliographic record
Abstract
Raw Data Files for the Following Publication: Collateral Sensitivity Profiling in Drug-Resistant Escherichia coli Identifies Natural Products Suppressing Cephalosporin Resistance\n\nE_coli_CSP_Antibioitic_MIC_R1.csv; E_coli_CSP_Antibioitic_MIC_R2.csv; E_coli_CSP_Antibioitic_MIC_R3.csv:\n\nRaw MIC values for all 80 antibiotic compounds against wild type and 29 drug-resistant strains of E. coli, as specified in the main manuscript. Antibiotics are arranged along the horizontal axis with their structural class indicated one row above. Drug-resistant strains, along with their mutant gene and strain designation, are arranged along the vertical axis. All values are in micromolar. >128 denotes MIC values greater than 128 micromolar.\n\nE_coli_CSP_NP_Library_OD600.csv:\n\nRaw OD600 absorbance values from CSP screening of 6,195 natural product extract prefractions against wild type and 29 drug-resistant strains of E. coli, as described in the main manuscript. Drug-resistant strains are arranged along the horizontal axis. Extracts are arranged along the vertical axis. Extracts that begin with RLCA denote Burkoholderia-derived extracts. Extracts that begin with RLUS denote marine Actinobacterial extracts. Extracts with the same letter and number code but different alphabetical character at the end (A to F) denote different prefractions of the same extract. All values represent the difference of the T20 and T0 measurements.
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.
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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.862 | 0.798 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".