MétaCan
Menu
Back to cohort
Record W7082970737 · doi:10.3929/ethz-c-000052687

Evolution of deepwater mixing and weathering inputs in the central Atlantic Ocean over the past 33 Myr

2003· other· en· W7082970737 on OpenAlexaboutno aff

Bibliographic record

VenueRepository for Publications and Research Data (ETH Zurich) · 2003
Typeother
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsNorth Atlantic Deep WaterFracture zoneMid-Atlantic RidgeBottom waterThermohaline circulationAntarctic Bottom WaterWater massDeep sea

Abstract

fetched live from OpenAlex

The isotopic composition of Nd in present-day deep waters of the central and northeastern Atlantic Ocean is thought to fingerprint mixing of North Atlantic Deep Water with Antarctic Bottom Water. The central Atlantic Romanche and Vema Fracture Zones are considered the most important pathways for deep water exchange between the western and eastern Atlantic basins today. We present new Nd isotope records of the deepwater evolution in the fracture zones obtained from ferromanganese crusts, which are inconsistent with simple water mass mixing alone prior to 3 Ma and require additional inputs from other sources. The new Pb isotope time series from the fracture zones are inexplicable by simple mixing of North Atlantic Deep Water and Antarctic Bottom Water for the entire past 33 Myr. The distinct and relatively invariable Nd and Pb isotope records of deep waters in the fracture zones appear instead to have been controlled to a large extent by contributions from Saharan dust and the Orinoco/Amazon Rivers. Thus the previously observed similarity of Nd and Pb isotope time series from the western and eastern North Atlantic basins is better explainable by direct supply of Labrador Seawater to the eastern basin via a northern pathway rather than by advection of North Atlantic Deep Water via the Romanche and Vema Fracture Zones.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.335
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueRepository for Publications and Research Data (ETH Zurich)Same topicDigital Economy and Work TransformationFrench-language works237,207