MétaCan
Menu
Back to cohort

Manure Management

2011· book-chapter· en· W4234319373 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueASSA, CSSA and SSSA · 2011
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsManureEnvironmental scienceAgronomyNutrientFertilizerAgricultureOrganic matterLivestockWater qualityChemistryBiologyEcology

Abstract

fetched live from OpenAlex

This chapter examines the pros and cons of manure and peruses some of the challenges facing its utilization in farming practices. Manure adds nutrients mainly in the form of N and phosphorus (P) as well as K, in addition to a host of minor and trace elements. Manure improves soil physical properties such as water retention, aggregate stability, and infiltration. Nitrogen losses from manure in the form of ammonia begin as soon as it is excreted. Livestock manure applications that supply P in excess of crop requirements can increase soil P concentration and subsequently P loss to surface water. Protection of water resources adjacent to land receiving manure containing enteric microorganisms, veterinary pharmaceuticals, and hormones requires that these contaminants be killed, degraded, sequestered, or otherwise inactivated in the soil. The nutrients and organic matter in manure are beneficial to crop production by improving nutrient supply and soil quality when land applied.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.006

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.

Opus teacher head0.027
GPT teacher head0.233
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it