222 Validation of Brix for predicting sugar concentration of alfalfa and orchardgrass
Bibliographic record
Abstract
Abstract The objective of this study was to evaluate the accuracy of Brix in predicting sugar concentrations in fresh alfalfa (Medicago sativa L.; ALF) and orchardgrass (Dactylis glomerata L.; OG) forages to be used as an inexpensive and rapid field-level assessment of relative energy in forages. In a 2-yr study, fresh forages samples from ALF and OG monoculture pastures in central Pennsylvania, U.S. were collected once monthly from May to September. Samples were immediately evaluated for Brix values with a hand-held digital refractometer, and the remaining biomass was frozen immediately with liquid N to halt cellular respiration. Samples were lyophilized and analyzed for individual sugars and wet chemistry nutritive analyses. Brix values were correlated with wet chemistry analyses using the PROC CORR procedure in SAS, with significance established at P < 0.05. Brix was positively correlated (P < 0.05) with total and individual sugars in ALF during late spring and late summer (0.49 to 0.93), but correlations were non-existent (P > 0.05) or negative (-0.55 to -0.73; P < 0.05) in mid-summer. Brix values had moderate to strong negative correlations (P < 0.05) to NDF and ADF in ALF (-0.57 to -0.71). Conversely, Brix values did not correlate (P > 0.05) to any notable sugar parameters in OG beyond the first sampling date (0.60 to 0.74; P < 0.05). Brix was not correlated (P > 0.05) to NDF and ADF in OG. In summary, Brix values did not consistently predict sugar concentrations in fresh ALF and OG forages. Because Brix measures dissolved solids in solution (not just sugars), Brix readings collected from crushed ALF or OG samples may be confounded by fibrous fractions found in the solution as well as decreased sugar concentrations compared with fruit crops. Brix accuracy may also be dependent on seasonal temperature patterns, plant growth stage, and daily weather patterns. Other solutions should be investigated that rapidly assess sugar profiles and nutritive values of fresh forages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".