Geomorphology and style of plateau icefield deglaciation in fjord terrains: the example of Troms‐Finnmark, north Norway
Bibliographic record
Abstract
Abstract In spite of a widespread distribution, the way in which plateau icefields affect the glaciation and deglaciation of adjacent terrains is not particularly well‐known. This paper aims to identify how the deglaciation of the fjord and plateau terrain of north Norway has influenced the glacial geomorphology and relative sea‐level history of both local and adjacent areas and so serve as a model for interpreting similar areas along the continental margins of northwest Europe and elsewhere. The identification of moraines and their relationships with the Main shoreline of northern Norway allows the margins of the Øksfjordjøkelen, Svartfjelljøkelen and Langfjordjøkelen plateau icefields to be identified in the adjacent terrains. In locations where ice margins are uncertain, it is also possible to reconstruct ice limits by means of glacier models appropriately constrained by known local conditions and dates. Earlier glacier margins, characterised in north Norway by ice shelves floating in the local inlets of major fjords, also can be related to known regional shorelines. The distribution of high shoreline fragments, augmented by radiocarbon dates, helps show the extent to which inter‐island channels and outermost parts of fjords can become deglaciated relatively early in comparison with published maps of regional deglaciation. Plateau‐icefield‐centred glaciation became important sometime after 14 00014C yr BP and was characterised by glacier readvances up to, and in some locations beyond, earlier moraines and raised marine features. Although overlooked until recently, the identification of the influence of plateau icefields on local glaciation, and their interaction with local and regional marine limits, is of great importance in accurate palaeoenvironmental reconstruction. Copyright © 2002 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".