Data presentation, interpretation, and communication
Bibliographic record
Abstract
Four crops were grown in three soil types at four sites near Truro, Nova Scotia in 1998. Water pH measurements and the Adams‐Evans (A‐E) Lime Requirement Test (LRT) suggested amounts of limestone required to bring the soil pH to 6.5. However, to evaluate the accuracy of three LRT procedures (A‐E, Shoemaker, McLean & Pratt, and Mehlich), a range of lime rates (0 to 12 MT/ha) was chosen for each crop at each site. Fertilizers were applied to each plot based on the present Nova Scotia Soil Test Recommendations. Whole plant or leaf tissue was sampled at Zadoks 77 (spring wheat), Zadoks 85 (barley), ear development (sweet corn) and at each of 7 cuttings of turfgrass. The tissue samples were digested and analyzed by ICP for up to 10 elements and by a CNS Analyzer for N. Soil samples were taken at the final harvest and the soil pH was determined. Lime applications increased the pH of all plots, proportional to the application rates, however, the relationship was not always linear. Of the three tests, the SMP LRT most closely estimated the amount of lime required to increase the soil pH to 6.5; the other two tests underestimated the lime requirement. In this paper, only data concerning barley tissue nutrient content and uptake was related to soil pH; nutrient uptake was highest at pH 6.12 following a 6 t ha‐1 lime application. Another paper will describe how the lime applications affected the nutrient uptake of the other three crops.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.314 | 0.131 |
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".