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Record W1977788548 · doi:10.1007/s00531-010-0563-9

Heterozoan carbonates in subtropical to tropical settings in the present and past

2010· article· en· W1977788548 on OpenAlexaff
Hildegard Westphal, Jochen Halfar, André Freiwald

Bibliographic record

VenueInternational Journal of Earth Sciences · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of Toronto
FundersDeutsche Forschungsgemeinschaft
KeywordsCarbonateTemperate climateSeawaterGeologySubtropicsOceanographyCarbonate mineralsTropicsEarth scienceEnvironmental scienceGeochemistryEcologyDolomiteChemistry

Abstract

fetched live from OpenAlex

Water temperature has received considerable attention as steering factor for the genesis of different types of marine carbonate sediments. However, parameters other than temperature also strongly influence ecosystems and, consequently, the carbonate grain associations in the resulting carbonate rock. Among those factors are biological evolution, water energy, substrate, water chemistry, light penetration, trophic conditions, CO2 concentrations, and Mg/Ca ratios in the seawater. Increased nutrient levels in warm-water settings, for example, lead to heterotrophic-dominated associations that are characteristic of temperate to cool-water carbonates. Failure to recognize the influence of such environmental factors that shift the grain associations towards heterotrophic communities in low latitudes can lead to misinterpretation of climatic conditions in the past. Modern analogues of low-latitude heterozoan carbonates help to recognize and understand past occurrences of heterozoan warm-water carbonates. Careful analysis of such sediments therefore is required in order to achieve robust reconstructions of past climate.

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 score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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

Citations91
Published2010
Admission routes1
Has abstractyes

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