Bibliographic record
Abstract
Our understanding of the Laurentide Ice Sheet has been significantly improved by recent developments in theoretical models of ice sheets and ice dynamics, understanding of mechanisms of glacial erosion, transport and deposition, and mapping of glacial deposits and drift composition. Theoretical models have increasingly accommodated diverse glaciological, physiographic and geological factors that can affect ice flow, and have been used to reinterpret the geological record in terms of glacial processes. The glacial system of North America is described at scales from continental (thousands of square kilometres), to regional (hundreds of square kilometres), local (kilometres to tens of kilometres), and site-specific (hundreds of metres to kilometres). Drift prospecting is based on an understanding that indicators of economic mineralization recovered from glacial deposits can be traced back to their original bedrock source. Geochemical, mineralogical and lithological methods employed in the exploration for mineral deposits in glaciated terrain requires knowledge of past glacial flow direction(s), means of entrainment, and variations in the nature of glacial dispersal such as glacial sediment thickness, bedrock topography, bedrock erodibility, and basal glacial flow velocity gradient. Combined, this knowledge is used to determine the distance to the up-ice bedrock source from the identified down-ice dispersal train location. Over the last two decades, drift prospecting in northern Canada has significantly improved through the continued development of ice sheets models, and the acquisition of empirical evidence for ice-flow patterns, drift composition and glacial history. Increased attention to the dynamic nature of glacial dispersal centers and related ice-flow complexes has thus become increasingly important for mineral exploration in northern Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".