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Record W2501885029 · doi:10.1080/17445647.2016.1206840

Glacial geomorphology of the tableland east of the Andes between the Coyle and Gallegos river valleys, Patagonia, Argentina

2016· article· en· W2501885029 on OpenAlexfundno aff
Bettina Ercolano, Andrea Coronato, Pedro Tiberi, Hugo Corbella, Guillermina Marderwald

Bibliographic record

VenueJournal of Maps · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersUniversidad Nacional de la Patagonia AustralSimon Fraser UniversityConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsMoraineOutwash plainGeologyLandformGlacial periodGlacial landformGlacierFluvialGeomorphologyMeltwaterTerminal moraineRiver terracesPhysical geographyLast Glacial MaximumGeographyStructural basin

Abstract

fetched live from OpenAlex

We report the results of geomorphological mapping of the high tablelands of Patagonia east of the Andes and between the Coyle and Gallegos river valleys. The map covers a low-relief area of about 3400 km2 at a scale of 1:85,000. It contributes to knowledge of a landscape shaped by lobes of the Patagonian ice sheet and can be used as a tool for further Quaternary studies. The map was built from remote-sensing data, compiled, and analyzed in geographical information system packages and supported by fieldwork. It features glacial landforms including moraine ridges and belts, ground moraine, outwash plains, outwash fans, meltwater channels, traces of glacial lakes, and erratic boulders. Fluvial, aeolian, and volcanic landforms and structural features were also mapped to provide a complete understanding of the landscape. We define three glacial units that record the frontal positions of former piedmont glaciers. The criteria used to define these units are the morphology, maximum relief, boulder content of moraine ridges, and crosscutting relations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.216
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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