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Record W2791979324 · doi:10.2527/asasann.2017.620

620 Estimating gas volume from headspace pressure in a batch culture system

2017· article· en· W2791979324 on OpenAlexaff
Atmir Romero-Pérez, K. A. Beauchemin

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsVolume (thermodynamics)Concordance correlation coefficientMathematicsLinear equationQuadratic equationMean squared errorAnalytical Chemistry (journal)ChemistryStatisticsChromatographyThermodynamicsMathematical analysisPhysicsGeometry

Abstract

fetched live from OpenAlex

Estimation of headspace gas volume (GV) from headspace pressure (GP) in in vitro batch culture experiments conducted at our lab is currently done using equations obtained overseas (Mauricio et al., 1999). Thus, the objective of the present work was to generate an equation to estimate GV from GP based on data obtained under our own experimental conditions. Two independent batch culture runs were conducted on different days. Twelve different feed ingredients including forages, grains, and by-products were utilized. Headspace GP and GV were measured after 3, 6, 9, 12, 18, 24, 48, 60, and 72 h using a pressure transducer and graduated plastic syringes (20 or 50 mL) connected to a three-way stopcock, respectively. A total of 1811 pairs of headspace pressure-volumes were obtained. One half of the data set was used to generate linear, quadratic, and cubic equations with intercepts set to zero or not (six equations in total). The second half was used to evaluate estimated GV from obtained equations. The best equation (equation 1) was then compared against equations reported by Mauricio et al. (1999; equation 2) and Lopez et al. (2007; equation 3). Boyle's law adapted to our lab conditions was also evaluated (equation 4; 63 mL headspace volume; atmospheric pressure 13.15 PSI). Equations were evaluated using the r2 between the observed and predicted values, root mean square prediction error (RMSPE), concordance correlation coefficient (CCC), and error due to the disturbance or random variation (ED). A quadratic equation was the most precise and accurate among the 6 equations obtained (GV = -0.2232 + 4.8021GP + 0.0422GP2; r2 = 0.995; RMSPE = 0.64 mL; ED = 99.9%; CCC = 0.99). When compared with equations 2, 3, and 4, equation 1 ranked first, followed by equation 4 (GV = 4.79GP; r2 = 0.994; RMSPE = 1.10 mL; ED = 39.3%; CCC = 0.99), equation 3 (GV = 5.385GP; r2 = 0.994; RMSPE = 1.85 mL; ED = 13.8%; CCC = 0.98), and equation 2 (GV = 0.18 + 3.697GP + 0.0824GP2; r2 = 0.994; RMSPE = 3.33 mL; ED = 4.23%; CCC = 0.93). In conclusion, the quadratic equation obtained in the present study estimates GV precisely and accurately and can be used in further experiments conducted under similar conditions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.331
Teacher spread0.301 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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