Additional file 1 of Functional characterization of fungal lytic polysaccharide monooxygenases for cellulose surface oxidation
Bibliographic record
Abstract
Additional file 1: Figure S1: Product released by 5 μM C1 LPMOs after 16 h on PASC (0.1%), Avicel (1%), and SA-Avicel (1%)with 1 mM ascorbic acid as the electron donor. For each substrate, T. reesei cellulase cocktail was used to convert all C1-oxidized products into cellobionic acid, which was then quantified by HPAEC-PAD and reported as the total C1-oxidized ends generated (nanomoles per mg of starting fiber). Each bar is the average of three independent assays measured singly by HPAEC-PAD, with error bars indicating the standard error of the mean. Figure S2: Brightfield (A) and confocal (B) images of untreated SA-Avicel labelled using rhodamine chloride. 1% SA-Avicel with 1 mM of gallic acid was incubated at 50 °C for 24 h. Insoluble products were separated, labelled with fluorescent dye, and visualized using confocal microscopy. Figure S3: Process schematic for LPMO treatment of cellulosic substrates and subsequent soluble and insoluble product analysis. PASC (0.1%), Avicel (1%), and SA-Avicel (1%) were treated with LPMOs using either ascorbic acid, gallic acid, or cysteine as the electron donor. Insoluble products were separated from soluble products using centrifugation. For the soluble products analysis, HPAEC-PAD was used to annotate native and oxidized cello-oligosaccharide peaks. To quantify the C1-oxidized products, T. reesei cellulase cocktail was used to convert all C1-oxidized products into cellobionic acid, which was then quantified by HPAEC-PAD and reported as the total C1-oxidized ends generated (nanomoles per mg of starting fiber). For insoluble product analysis, separated insoluble products were labelled with either C1-specific or C4-specific fluorescent dye and subsequently visualized using confocal microscopy.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.840 | 0.195 |
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".