Defensive Armor of Potato Tubers: Nonpolar Metabolite Profiling, Antioxidant Assessment, and Solid-State NMR Compositional Analysis of Suberin-Enriched Wound-Healing Tissues
Bibliographic record
Abstract
The cultivation, storage, and distribution of potato tubers are compromised by mechanical damage and suboptimal healing. To investigate wound-healing progress in cultivars with contrasting russeting patterns, metabolite profiles reported previously for polar tissue extracts were complemented by GC/MS measurements for nonpolar extracts and quantitative (13)C NMR of interfacial solid suspensions. Potential marker compounds that distinguish cultivar type and wound-healing time point included fatty acids, fatty alcohols, alkanes, glyceryl esters, α,ω-fatty diacids, and hydroxyfatty acids. The abundant long-chain fatty acids in nonpolar extracts and solids from the smooth-skinned Yukon Gold cultivar suggested extensive suberin biopolymer formation; this hypothesis was supported by high proportions of arenes, alkenes, and carbonyl groups in the solid and among the polar markers. The absence of many potential marker classes in nonpolar Atlantic extracts and interfacial solids suggested a limited extent of suberization. Modest scavenging activities of all nonpolar extracts indicate that the majority of antioxidants produced in response to wounding are polar.
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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 | 0.000 | 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) | 0.001 | 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 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".