A flowline map of glaciated Canada based on remote sensing dataThis paper is accompanied by a large foldout map entitled <i>A flowline map of glaciated Canada based on remote sensing</i> <i>data</i> (see pocket on back cover).
Bibliographic record
Abstract
The map A flowline map of glaciated Canada based on remote sensing data presents flowlines for the former Laurentide and Cordilleran ice sheets based on flow indicators derived from aggregated, flow-parallel landforms — drumlins and crag and tails, fluting, sinuous ridges and furrows, and rises. An extensive review introduces the concepts and evolution of flowline mapping at continental-ice-sheet and regional scales, emphasizing the use of new remote sensing data. Coherent, glaciologically plausible sets of flowlines mapped as flow tracts reflect large-scale flow structure in the paleo-ice sheets and demarcate fields of flow-parallel bedforms. In addition to flow reconstruction, mapped distributions of fields of glacial terrain types — hummocky terrain, Rogen terrain, and bedrock-dominant terrain — increase our power to interpret flowlines and, in turn, give evidence on the genesis of these terrains. End moraines and eskers also aid map interpretation. Landsat 7 Enhanced Thematic Mapper+ (ETM+) satellite images and Shuttle Radar Topography Mission (SRTM) hill shades provide the basic information for this flowline mapping in a Geographical Information System (ArcMap). Information on the Flowline Map allows us to develop conceptual models of ice sheets and to appreciate regional constraints on applications in mineral exploration, in aggregate and groundwater discovery and assessment, in soil and landform genesis, and in glaciology, paleoclimatology, and paleoceanography.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".