Growing medium soluble carbon and nitrogen influence xylem sap and soluble solid contents in greenhouse cucumber fruits
Bibliographic record
Abstract
Compelling evidence recently demonstrated that plants can take up nitrogen (N) as organic molecules. Yet, very little research addressed this issue in the context of organic horticulture, where N is provided as organic residues. Organic N and carbon (C) transported from roots to shoots could contribute significantly to the plant C and N accumulation. We posited that the type (organic or mineral) and amount of N fertilization affect the soluble organic N and C content of the growing medium, in turn influencing xylem sap N and C and fruit soluble solids content (SSC). To test this hypothesis, we collected growing medium, xylem sap, and fruit samples in a greenhouse cucumber crop grown in a peat-based growing medium and fertilized with organic (blood and feather meals) or mineral (ammonium nitrate) N fertilizers. The organic N source reduced growing medium concentrations of soluble mineral and organic N relative to the mineral source through microbial immobilization. Xylem sap C and N were positively linked to the soluble C and N contents of the growing medium, contributing to higher fruit SSC. A causal model is proposed, in which 62.6% of the variance observed in fruit SSC is explained by variation in mineral soluble N and soluble organic C in the growing medium and dissolved organic C and amino acids in xylem sap. Our results provide in situ indications that organic molecules in the growing medium are taken up by cucumber plants and contribute to fruit soluble solids in a context relevant to greenhouse horticulture.
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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".