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Record W4378470402 · doi:10.7185/geochemlet.2316

River chemistry reveals a large decrease in dolomite abundance across the Phanerozoic

2023· article· en· W4378470402 on OpenAlexaff
Jon M. Husson, L. A. Coogan

Bibliographic record

VenueGeochemical Perspectives Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPhanerozoicDolomiteAbundance (ecology)GeologyPaleontologyGeochemistryEcologyBiologyCenozoic

Abstract

fetched live from OpenAlex

The abundance of dolomite in ancient carbonate sediments, and its apparent rarity today, has important implications for the coupled Ca-Mg-C-cycles in seawater and global climate. Despite its importance, there are large differences between published records of dolomite abundance vs. geologic age, mainly due to complexities in adequately sampling heterogeneous bedrock. We overcome this issue by using dissolved Mg 2 and Ca 2 measurements in rivers draining carbonate-bearing bedrock. Because rivers weather broad areas, this approach integrates the geochemical composition of much larger volumes of carbonate compared to sample based methods. The average Mg/(Ca Mg) molar ratio in rivers declines with decreasing bedrock age, from 0.44 at 485 million year old (Ma) to 0.14 at 5 Ma, suggesting a decreasing percentage of dolomite in carbonate sequences across the Phanerozoic Eon. These data are hard to reconcile with any model that relies only upon oscillatory drivers to explain the dolomite abundance record, such as sea level or episodic expansions of ocean anoxia, and have important implications for the oceanic Mg cycle.

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.267
Teacher spread0.255 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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