620 Estimating gas volume from headspace pressure in a batch culture system
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, not a consensus.
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".