Networking 13 Berry Minerals to Sustain a High Yield of Firm Cranberry Fruits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The N fertilization to reach high cranberry (Vaccinium macrocarpon) yields resulted in high proportions of soft berries. Our objective was to define the mineral nutrient balance of cranberry to reach a high yield of firm berries. The database comprised 393 observations on cv. ‘Stevens’. Berries were analyzed for total S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe, Al, and Si. Random Forest and XGBoost machine learning models were run to predict yield and firmness classes using raw concentrations, centered log ratios (clr) accounting for nutrient interactions, and weighted log ratios (wlr) that also considered the importance of each dual interaction. The wlr returned the most accurate models. The wlr standards elaborated from the high-yielding and nutritionally balanced subpopulation most often differed between the high-yield class and the high-firmness class. The wlr Cu level was significantly (p ≤ 0.01) too high to reach the high-yielding class in the nutritionally imbalanced subpopulation. There was excessive Al and shortage of Si and Mg to reach high berry firmness in the nutritionally imbalanced subpopulation (p ≤ 0.01), indicating the large influence of soil genesis on berry firmness. Despite statistical evidence, cranberry response to Al and Si corrective measures should be tested to elaborate site-specific recommendations based on soil and tissue tests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it