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
Effective nutrient management requires an accurate accounting of nutrients removed from soils in the harvested portion of a crop. Because the typical crop nutrient values that have historically been used may be different under current production practices, a study was conducted to measure nutrient uptake in grain harvested in 1998 and 1999 from 23 site‐years in the Mid‐Atlantic region of the USA. There were 10 hybrids included in the study, but each site grew only one hybrid each year. Corn ( Zea mays L.) production practices followed local state extension recommendations. Minimum, maximum, and mean corn grain yields were 4.9, 16.7, and 10.3 Mg ha −1 . Nutrient concentrations were determined on grain samples oven‐dried at 70°C for 24 h. Minimum, maximum, and median nutrient concentration values were as follows: 10.2, 15.0, and 12.9 g N kg −1 ; 2.2, 5.4, and 3.8 g P kg −1 ; 3.1, 6.2, and 4.8 g K kg −1 ; 0.13, 0.45, and 0.28 g Ca kg −1 ; 0.88, 2.18, and 1.45 g Mg kg −1 ; 0.9, 1.4, and 1.0 g S kg −1 ; 9.0, 89.5, and 33.6 mg Fe kg −1 ; 15.0, 34.5, and 26.8 mg Zn kg −1 ; 1.0, 9.8, and 5.3 mg Mn kg −1 ; 1.0, 5.8, and 3.0 mg Cu kg −1 ; and 2.3, 10.0, and 5.5 mg B kg −1 . Median nutrient uptake values found in this study are similar to commonly used book values, but there was considerable variation among samples of corn grain. Concentrations of P and K in grain were positively associated with yield level, and concentrations of grain P were positively correlated with Mehlich‐3 soil test P. The variability in nutrient removal values seen in this study, even for the same hybrid, raises questions about the usefulness of average values for estimating crop nutrient removal across a range of cropping conditions. Research is needed to identify or develop a means to correct for the sources of variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
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 it