Bibliographic record
Abstract
Cratonic regions of the world provide rich libraries of remnant impact craters, and Canada is one of the richest among them.For nearly six decades, Canadian scientists have harnessed this natural resource to produce some of the best geologic descriptions of impact craters in the world.As described in Impact structures in Canada, this initiative began at the Dominion Observatory under the direction of C. S. Beals in the 1950s.Success was achieved early in the program with a comprehensive geophysical and drilling campaign at Brent crater, which remains one of the bestcharacterized simple craters in the world.Craters in Canada have a broad distribution of ages, ranging from 1 to 1850 Ma, providing a detailed measure of specific events that shaped the Earth over nearly 2 billion years, and broader insights into processes that shaped Earth throughout its entire 4.5-billion-year history.Although the record of cratering in Canada is rich, impact structures have been partially eroded.Rather than being a detriment, however, this created a special opportunity to explore subsurface components of impact craters, providing important information about the structural evolution that occurs as a function of crater size.It also provided an opportunity to study the distribution of shock-metamorphosed products, ranging from shatter cones to multi-kilometer-thick impact-melt sheets.These studies have, in turn, led to empirical scaling relationships that are applied to impact sites throughout the world and on other planetary surfaces.This book synthesizes the results of those studies in an accessible way.The volume begins with a 22-page introduction about impact craters and impact-cratering processes.The introduction is very broad, addressing a series of important issues: the spatial, temporal, and size distribution of craters; cratering rate; crater morphology; processes associated with the excavation and modification of craters, including the deposition of impactites; subsolidus shock effects; impact melting; distribution of shock metamorphism; and gravity, magnetic, and seismic signatures of impact sites.I particularly like the discourse in this chapter, because Grieve explains the development of many concepts and how they were influenced by geologic evidence recovered from impact sites.He also identifies several issues that still need to be addressed.The introduction is so complete that it has become the first paper I recommend to new students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".