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Record W2067145094 · doi:10.1081/css-200043187

Compositional Analysis of Cattle Manure During Composting Using a Field‐Portable Near‐Infrared Spectrometer

2005· article· en· W2067145094 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

VenueCommunications in Soil Science and Plant Analysis · 2005
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsManureCompostStockpileEnvironmental scienceFeedlotRaw materialStrawNutrientPulp and paper industryComposition (language)BiogasWaste managementChemistryAgronomyAnimal scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Composting of livestock manure is an effective method for managing the nutrients for agronomic purposes and reducing environmental and human health risks. Capability to analyze the biowastes on‐site at the start of, periodically during, and at the end of composting could facilitate managing the composting process and increase the value of the end products. Near‐infrared spectroscopy (NIRS) is well known for its capability to analyze organic substances rapidly and cost‐effectively. This study was conducted to explore the capability of a field‐portable NIR spectrometer to determine nutrient composition of beef feedlot manure when raw, stockpiled (not turned), and composted (windrowed and turned). Over a 2‐yr period, beef feedlot manure mixed with bedding (wheat straw) was sampled annually at cleanout, after storage for some months in a large stockpile, and from windrows subjected to active thermophilic composting. Samples were dried and ground and scanned with the field‐portable Corona 45 VIS NIR (visible/near‐infrared) spectrometer (Carl Zeiss, Germany) from 360 to 1690 nm. NIRS was found useful in two ways. Classification analysis (Soft Independent Modeling of Class Analogy [SIMCA]) using the spectral data alone showed that stockpiling the manure did not change in composition significantly whereas compost was significantly, different from and less variable than raw or stockpiled manure. Second, by combining spectral and compositional data representing raw, stockpiled, and composted manure for both years, useful calibrations were developed for total C, organic C, total N, C:N, S, K, and pH. These calibrations can be used to rapidly predict these constituents in new samples. The calibration for P may be useful for screening, but those for nitrate+nitrite, available P, and Na were not successful. On the basis of analysis of dried samples, the field‐portable NIR spectrometer was found successful for the rapid determination of C, N, and several other parameters in raw, stockpiled, and composted manure. For application of the technology on‐site, these results need to be confirmed in further studies using moist, "as is" manure and compost samples.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.010
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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