Surface Soil Salinity and Soluble Salts after 15 Applications of Composted or Stockpiled Manure with Straw or Wood-Chips
Bibliographic record
Abstract
The influence of 15 annual applications of composted (CM) or stockpiled (SM) beef feedlot manure with straw (ST) or wood-chip (WD) bedding on electrical conductivity (EC), soluble cations and anions (Na, K, Ca, Mg, SO4-S, Cl), sodium adsorption ratio (SAR), potassium adsorption ratio (PAR), and pH of a clay loam soil (0–15 cm) in southern Alberta was examined in an irrigated barley silage cropping system. Manure type (CM versus SM) had a significant effect on certain soil salinity parameters. Calcium, Mg, Na, K, and SO4-S were significantly (p ≤ 0.05) greater for SM- than CM-amended soils for certain bedding materials and rates. Electrical conductivity, concentration of soluble cations and anions (Na, K, SO4-S, Cl), SAR, PAR, and pH in the surface soil were greater for ST than WD bedding. Two exceptions were Ca and Mg, where soil concentrations were generally greater for WD than ST. Salinity parameters were greater with increased application rate, and greater for amended than unamended soils. Overall, bedding had considerably more significant effects on soil salinity parameters compared to manure type. Wood-chip bedding may be a management tool for feedlots to lower EC, soluble cations and anions, and pH of surface soils.
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".