1H NMR metabolic profiling dataset of spiny chicory (Cichorium spinosum L.) exposed to abiotic stresses
Bibliographic record
Abstract
The data presented here were derived by 1H NMR metabolic profiling of stamnagathi (Cichorium spinosum L.) plants following treatments with different isosmotic salt solutions; eight saline nutrient solutions with two different levels of total molar concentrations, which were obtained by adding different amounts of NaCl, KCl, Na2SO4 or CaCl2 to the replenishment nutrient solution, were applied. The 1H NMR metabolite profiles of stamnagathi plants’, which are included in this article, were recorded 56 days after transplanting. Since stamnagathi is a niche product combining unique taste and superior phytonutrient content (e.g. vitamins C and K1, lutein, β-carotene, tocopherols, phenolic acids, fatty acids, minerals, and glutathione), the dataset could serve as a reference for future metabolomics studies related to the investigation of the effects of the four salinity sources on the plant's metabolism. Also, the dataset could be a valuable resource for the discovery of validated biomarkers of the plant's tolerance to salinity stress and responses to new plant protection products (e.g. bioelicitors). The dataset support the research article “Salinity source alters mineral composition and metabolism of Cichorium spinosum” authored by Ntatsi et al., (2017) [1].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".