Bedding and Within‐Pen Location Effects on Feedlot Pen Runoff Quality Using a Rainfall Simulator
Bibliographic record
Abstract
Soluble salts, nutrients, and pathogenic bacteria in feedlot-pen runoff have the potential to cause pollution of the environment. A 2-yr study (1998-1999) was conducted at a beef cattle (Bos taurus) feedlot in southern Alberta, Canada, to determine the effect of bedding material [barley (Hordeum vulgare L.) straw versus wood chips] and within-pen location on the chemical and bacterial properties of pen-floor runoff. Runoff was generated with a portable rainfall simulator and analyzed for chemical content (nitrogen [N], phosphorus [P], soluble salts, electrical conductivity [EC], sodium adsorption ratio [SAR], dissolved oxygen [DO], and pH) and populations of three groups of bacteria (Escherichia coli, total coliforms, total aerobic heterotrophs at 27 degrees C) in 1998 and 1999. Bedding had a significant (P < or = 0.05) effect on NH4-N concentration and load in 1999, SO4 load in 1998, SO4 concentration and load in 1999, and total coliforms in both years; where these three variables were higher in wood than straw pens. Location had a significant effect on EC and concentrations of total Kjeldahl nitrogen (TKN), Na, K, SO4, and Cl in 1998, and total coliforms in both years. These seven variables were higher at the bedding pack than pen floor location, indicating that bedding packs were major reservoirs of TKN, soluble salts, and total coliforms. Significantly higher dissolved reactive phosphorus (DRP), total P, and NH4-N concentrations and loads at the bedding pack location in wood pens in 1998, and a similar trend for TKN concentration in 1999, indicated that this bedding-location treatment was a greater source of nutrients to runoff than the other three bedding-location treatments. Bedding, location, and their interaction may therefore be a potential tool to manage nutrients, soluble salts, and bacteria in feedlot runoff.
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 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".