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Record W3021796770 · doi:10.3390/ani10050807

Effects of Two Manure Additives on Methane Emissions from Dairy Manure

2020· article· en· W3021796770 on OpenAlexafffund
Jessie Cluett, Andrew VanderZaag, Hambaliou Baldé, S. M. McGinn, Earl Jenson, Alexander C. Hayes, Sylvanus Ekwe

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsAgriculture and Agri-Food CanadaDalhousie University
FundersAgriculture and Agri-Food Canada
KeywordsManureMethaneChemistryAnimal scienceGreenhouse gasMethane emissionsSignificant differenceIncubationChicken manureCelluloseManure managementAgronomyMathematicsBiology

Abstract

fetched live from OpenAlex

Liquid manure is a significant source of methane (CH4), a greenhouse gas. Many livestock farms use manure additives for practical and agronomic purposes, but the effect on CH4 emissions is unknown. To address this gap, two lab studies were conducted, evaluating the CH4 produced from liquid dairy manure with Penergetic-g® (12 mg/L, 42 mg/L, and 420 mg/L) or AgrimestMix® (30.3 mL/L). In the first study, cellulose produced 378 mL CH4/g volatile solids (VS) at 38 °C and there was no significant difference with Penergetic-g® at 12 mg/L or 42 mg/L. At the same temperature, dairy manure produced 254 mL CH4/g VS and was not significantly different from 42 mg/L Penergetic-g®. In the second lab study, the dairy manure control produced 187 mL CH4/g VS at 37 °C and 164 mL CH4/g VS at 20 °C, and there was no significant difference with AgrimestMix (30.3 mL/L) or Penergetic-g® (420 mg/L) at either temperature. Comparisons of manure composition before and after incubation indicated that the additives had no effect on pH or VS, and small and inconsistent effects on other constituents. Overall, neither additive affected CH4 production in the lab. The results suggest that farms using these additives are likely to have normal CH4 emissions from stored 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.391

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.232
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2020
Admission routes2
Has abstractyes

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