Le peintre d´Amasis et la glyptique mésopotamienne protodynastique: réflexions sur l´iconographie du tissage et sur quelques prototypes orientaux méconnus: réflexions sur l´iconographie du tissage et sur quelques prototypes orientaux méconnus
Bibliographic record
Abstract
Odor and greenhouse gas (GHG) emissions from stored pig (Sus scrofa) manure were monitored for response to changes in the crude protein level (168 or 139 g kg(-1), as-fed basis) and nonstarch polysaccharide (NSP) content [i.e., control, or modified with beet pulp (Beta vulgaris L.), cornstarch, or xylanase] of diets fed to pigs in a production setting. Each diet was fed to one of eight pens of pigs according to a 2 x 4, full-factorial design, replicated over three time blocks with different groups of animals and random assignment of diets. Manure from each treatment was characterized and stored in a separate, ventilated, 200-L vessel. Repeated measurements of odor, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions from the vessels were taken every two weeks for eight weeks. Manure from high-protein diets had higher sulfur concentration and pH (P < or = 0.05). High-NSP (beet pulp) diets resulted in lower manure nitrogen and ammonia concentrations and pH (P < or = 0.05). Odor level and hedonic tone of exhaust air from the storage vessel headspaces were unaffected by the dietary treatments. Mean CO2 and CH4 emissions (1400 and 42 g d(-1) m(-3) manure, respectively) increased with lower dietary protein (P < or = 0.05). The addition of xylanase to high-protein diets caused a decrease in manure CO2 emissions, but an increase when added to low-protein diets (P < or = 0.05). Nitrous oxide emissions were negligible. Contrary to other studies, these results do not support the use of dietary protein reduction to reduce emissions from stored swine manure.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".