Dynamic process model of palsa genesis and development based on geomorphologic investigations at the Boundary Ridge Palsa Bog near Schefferville, Quebec
Bibliographic record
Abstract
Numerical simulation of forming processes has been an important means for material selection, tool design, and process optimization. A critical component of simulation, however, is an accurate material constitutive model, describing the response of the material under possible modes of deformation. The accuracy, in turn, is linked to the tests and techniques applied for identification of constitutive models: the more elaborate the identification, the more reliable the material parameters. For textile composites, uncontrollable factors such as contact friction, misalignment, slip, variations in local fiber volume, and tow compaction are sources that generate considerable scatter in the response of fabrics. Accordingly, characterization methods occasionally suffer from non-repeatability of test data even under similar testing conditions. Furthermore, it is typical that different deformation modes result in different sets of material parameters. If variance of material response within the replication of tests and deformation modes is neglected, then the identification of model parameters can be far from the true material behavior. In order to confront the above shortcomings, this work is an attempt to elaborate on the characterization of textile composites using a new inverse method by means of a signal-to-noise weighting scheme, and two constitutive models by means of a phenomenological invariant-based approach. A full identification of the developed constitutive models for a typical woven fabric is applied using the introduced inverse method and a set of data from standard testing methods, with close attention to the behavior of the composite constituents in a macro level. Particularly, the effects of fiber-resin interactions and fiber misalignment are introduced. A novel modified picture frame test is also studied and used for validating the models. From the results of this work, it is expected that the use of a number of test methods simultaneously and the inclu
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".