Bibliographic record
Abstract
Christopher Neuzil, Discussion Editor “Porosity and Permeability in Sediment Mixtures,” by Patrick J. Kamann, Robert W. Ritzi, David F. Dominic, and Caleb M. Conrad, July–August 2007 issue, v. 45, no. 4: 429–438. Kamann et al. (2007) address a very interesting and important topic. However, the presentation missed certain key references and concepts related to porosity and permeability of mixtures. The paper appears to convey the idea that a granular material has a unique porosity. However, even with equal spheres, porosity can range from a minimum of about 26% for simple cubic packing to a maximum of nearly 48% for hexagonal packing (e.g., Smith et al. 1929). The porosity is also strongly related to various physical properties such as elastic modulus, shear strength, and more generally stress-strain behavior. The relationships between the grain-size distribution curve and the associated range of porosity values have long been studied in many areas for binary, ternary, and multimodal mixtures (e.g., Furnas 1928; Burmister 1938; Hutchinson and Townsend 1961; Epstein and Young 1962; Lees 1970; Peronius and Sweeting 1985; Yu et al. 1993; Yu and Zou 1997; Rassouly 1999; Liu and Ha 2002). The Kamann et al. results should be modified to take into account a range of porosity (minimum and maximum values) that depends on the densification energy (i.e., natural or imposed stress state). The extreme values of porosity can be obtained using detailed procedures defined in commonly used standards (American Society of Testing and Materials 2007a, 2007b). In the paper, the preparation method does not appear to follow a specific standard; at least, it is not sufficiently described to be reproduced by others. With reference to prior research work, the authors could have mentioned that (1) the porosity range of a binary mixture depends on the size ratio and the percentage of each component; (2) the porosity of a sediment mixture for a given preparation and placement (compaction) mode cannot be obtained by a linear combination of the porosity of the mixture components for the same preparation and placement mode; and (3) the average grain diameter does not determine the range of porosity values. The latter can nonetheless be predicted using various characteristics of the grain-size distribution and the particle shape and roughness. When dealing with mixtures (and materials with a widely distributed grain size), it is also important to recognize conditions that produce segregation. It is unfortunate that the paper also omitted a large number of studies that have investigated means to predict the permeability of binary and multimodal mixtures (e.g., Leitzelement et al. 1985; Chapuis 2002). For clarifying the confusion around the Kozeny-Carman equation, one may refer to Chapuis and Aubertin (2003, 2004). For linking the predictive equations of Hazen and Kozeny-Carman, the reader may refer to Mbonimpa et al. (2002), Chapuis (2004), and Aubertin et al. (2005). Finally, it is worth mentioning that, since 1989, many scientific associations working on particle packing have gathered at the International Conferences on Micromechanics of Granular Media to avoid the lack of communication that had resulted in “replicated effort in learning the same basic principles over and over” (German 1989).
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".