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Northern Hemisphere ice sheets during the M2 Pliocene glacial

2022· preprint· en· W4285491434 on OpenAlexaboutno aff
Daniel J. Hill, Aisling M. Dolan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsGlacial periodIce sheetGeologyNorthern HemisphereLast Glacial MaximumIce-sheet modelOceanographySea iceClimatologyCryosphereIce streamPaleontology

Abstract

fetched live from OpenAlex

The large glacial period that immediately precedes the mid-Pliocene Warm Period (mPWP) is a ~0.6‰ benthic oxygen isotope shift at 3.3Ma, known as M2. The excursion is roughly equivalent to 60m of sea level drop or 40msle (metres sea level equivalent) more ice than today. However, there is significant uncertainty in both these values and the potential locations of any large volumes of ice. Previous modelling studies have either used Last Glacial Cycle analogues for the M2 ice sheets or failed to reproduce the large Northern Hemisphere ice sheets implied by global ice volume proxies. Here we present new climate and ice sheet models simulating the M2 glacial period, by lowering Pliocene atmospheric carbon dioxide concentrations and selecting specific Pliocene orbital forcing. These climates are sufficiently cold to produce ice sheets in North America and northern Europe within our modelling framework. The largest components of these ice masses are centred over regions with significant landscape differences between Pliocene and present day, including the Hudson Bay, Canadian Archipelago and Barents Sea. This suggests that the M2 glacial may have had very different initiation locations to the most recent glacial cycles and that Pliocene palaeogeographic changes may be key to understanding the M2 glacial.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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