Bibliographic record
Abstract
Columnar joints form as a brittle relaxation response to tensile stresses within cooling lava flows and magma bodies, and are found in lavas that vary greatly in chemistry and outcrop geometry. However, columnar joints do not form in all cooling igneous rocks, and the specific conditions under which columnar joints form are unknown. In this study, outcrops containing columns in the Cheakamus Valley basalt flows near Whistler, BC are studied, and the size, orientation, and distribution of columns is recorded. Forward numerical models using the finite element method are created with Matlab using the Partial Differential Equation Toolbox to model the outcrops in the Whistler field area, and determine the cooling rates (∂T/∂t) and thermal gradients (∂T/∂x) experienced by the lava flows during their formation. High temperature experimentation involving basalt rock samples is then used to determine the cooling rates and thermal gradients present during the cooling of these samples under a variety of naturally occurring conditions. This study finds that noticeable differences in the distribution of columns within an outcrop occur only when there are large differences in cooling rates between the upper and lower outcrop surfaces. Modeling shows that the cooling rates must differ by approximately an order of magnitude. High temperature experiments show that extremely high cooling rates (especially in the small sample sizes used in this study) between approximately 700 to 800 ˚C are necessary for the formation of columnar joints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".