Additional file 8 of Determinants of substrate specificity in a catalytically diverse family of acyl-ACP thioesterases from plants
Bibliographic record
Abstract
Additional file 8: Fig. S4. Full-length membranes used for immunodetection of ALT proteins following transfer from Tris-Tricine SDS-PAGE gels and staining with Ponceau S, and following immunodetection with anti-T7 mouse monoclonal primary antibody and an anti-mouse horseradish peroxidase-conjugated secondary antibody. Ponceau-stained membranes were imaged under white light with an exposure time of 1/30s. Probed membranes were imaged at 59 s exposure using a BioRad ChemiDoc XRS+ system with ImageLab v6.0.1 software. Black rectangles delineate where Ponceau-stained membranes were cut prior to being probed with antibody, and the boundaries of probed membranes. Red rectangles indicate regions where membranes were cropped to construct Fig. 5. Graphs relating ALT protein accumulation to total FA + MK productivity in E. coli are shown to the right of each membrane. Total FA + MK productivity of ALTs, in units of nmol / mL OD600 is represented by grey bars, with bar height corresponding to values on the left-hand vertical axis. Dots (●) indicate relative expression levels of the heterologously expressed ALT proteins, with values on the right-hand vertical axis. Thioesterase productivity values reported are the average of triplicate samples, with error representing ± SE (data shown in Table S2). Relative protein expression levels were calculated by normalizing ALT band volume (intensity) on antibody-probed membranes to total lane volume on Ponceau S-stained membranes in ImageLab v6.0.1 software. Unlabelled lanes represent E. coli strains expressing ALT constructs that were not analyzed further in this work.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.877 | 0.256 |
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".