The effect of liquid hog manure and commercial fertilizer on nutrient movement in a sandy soil
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
A two-year field study was initiated n the spring of 2OO2 to investigate the effects of liquid hog manure and commercial fertilizer applications on the leaching losses of soil water and nutrient on a field ste situated over the Assiniboine Delta Aquifer (ADA) at Carberry, MB.The study used two methods for determining soil water and NO3-N leaching losses: traditional soil sampling and large intact sol core lysimeters.By the end of the two years, the highest rate of hog manure, cropped fertilizer and fertilizer fallow plots had 23g,2sg and 22T kg No3-N ha-1, respectively, within the root zone, which was significantly higher than the control, 2500 gal ac-1 and 5000 gal ac-1 plots (61 ,107 and 123 kg NO3-N ha-1, respectively).The highest rate of manure and the two commercial fertilizer treatments had greater cumulative amounts of NO3-N within the root zone than what Manitoba Agriculture guidelines considers excessive (i.e.168 kg NO3-N ha- 1.Soil sampling showed leaching losses of nitrate-nitrogen from all treatments, however little movement of Mehlch-3 phosphorus was observed after two years of this study.There was a distinct rate effect as nitrate-nitrogen concentrations of B, 1 1 , 15 and 23 mg NOa-N kg-1 were found in the control, 2SO0 gal ac-1, 5000 gal ac-1 and 7500 gal ac-1 treatments at the 20-30 cm depth after two years, respectvely.Nitrate-nitrogen concentration was increased by the application of commercial fertilizer (i.e. 12 mg kg-1) above both the control (i.e.2 mg kg-1) and manure (ie.6 mg kg-1 plots at a depth of 75cm after two years.However below
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) | 0.000 | 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