Cretaceous Ocean Redbeds
Bibliographic record
Abstract
Abstract The occurrence of marine red beds has been known for at least 140 years, since Stúr (1860) and Gümbel (1861) first described them from the Púchov beds in the Carpathians and the Nierental beds in the Eastern Alps. A few biostratigraphic and sedimentological studies followed, particularly in European countries. However, detailed investigations on paleoceanographic and paleoclimatic implications related to Cretaceous marine red beds were initiated by Prof. Chengshan Wang, Dr. Xiumian Hu, and their colleagues. This collection of papers resulted from two collaborative research projects funded in part by UNESCO/IUGS International Geosciences Project IGCP 463 and IGCP 494. The IGCP 463 “Upper Cretaceous Oceanic Red Beds: Response to Ocean/Climate Global Change” (2002-2006) was led by Prof. Chengshan Wang (China University of Geosciences, Beijing, China), Prof. Massimo Sarti (Universitá Politecnica delle Marche, Italy), Dr. Robert Scott (University of Tulsa and Precision Stratigraphy Associates, USA), and Prof. Luba Jansa (Dalhousie University, Canada). The objective of IGCP 463 was to study major paleoceanographic phenomena recorded by sedimentary sequences in the world oceans. Cretaceous deposition changed several times from widespread organic-carbon-enriched shales that indicate a dysoxic to anoxic deep ocean environment, to mostly reddish clays and marls deposited in an oxic marine environment during the Late Cretaceous.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".