Development of Standard Concentrations of Foliar Nutrients for Saskatoon
Bibliographic record
Abstract
Soil and foliar samples were collected from saskatoon orchards in Saskatchewan, Manitoba, and Alberta, Canada from 1997 to 1999 and analyzed for macro- and micronutrient content. Foliar samples were collected twice a month from the end of May until September in 1997 to examine the pattern of change in foliar nutrient concentrations throughout the season and to determine the most stable time period for foliar sampling. This period was determined to be from the last week in July until mid-August. Nutrient concentrations of foliar samples collected during this period from 1997 to 1999 were summarized according to the mean, median, minimum, and maximum values. Mean foliar nutrient concentrations were as follows: 2.48% nitrogen (N), 0.18% phosphorus (P), 1.15% potassium (K), 0.15% sulfur (S), 1.52% calcium (Ca), 0.50% magnesium (Mg), 6.9 ppm copper (Cu), 106 ppm iron (Fe), 124 ppm manganese (Mn), 16 ppm zinc (Zn), and 27 ppm boron (B). A number of significant positive correlations were found between soil and foliar levels of a nutrient, with the majority of these correlations occurring for the nutrients Cu, P, and Mn. Another study conducted during 2001–2002 examined differences in the foliar nutrient concentrations of the saskatoon cultivars ‘Smoky’ and ‘Thiessen’ sampled from nine orchards in Saskatchewan. Foliar concentrations of N, K, S, Ca, Mg, Cu, Mn, Zn, and B were significantly higher in ‘Smoky’ than in ‘Thiessen,’ whereas foliar K content was higher in ‘Thiessen’ than in ‘Smoky’.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".