Chapter 4 Chemostratigraphy and the Neoproterozoic glaciations
Bibliographic record
Abstract
Abstract Although the pre-glacial Proterozoic isotopic record is poorly constrained, it is apparent that the chemical and isotopic composition of the oceans began to change during the early to mid-Neoproterozoic and experienced considerable fluctuations alongside climatic instability during much of the subsequent Cryogenian and Ediacaran periods. The earliest known large negative δ 13 C excursion appears to post-date 811 Ma and fluctuations became progressively more extreme, culminating in the late-Ediacaran ‘Shuram–Wonoka’ anomaly. The negative excursions are commonly associated with pre-glacial and post-glacial times, while extremely high δ 13 C values are characteristic of strata between glaciations. The precise causal mechanism for these excursions is subject to debate. Seawater 87 Sr/ 86 Sr rose during the Neoproterozoic, with abrupt increases following deglaciation consistent with enhanced weathering rates. Reported marine sulphate and pyrite δ 34 S data exhibit marked variation through this interval, although the changes are not always consistent within or between sedimentary successions of equivalent age. Iron-speciation studies indicate that much of this variation was caused by fluctuating and low sulphate concentrations in seawater, which at times led to the build-up of ferruginous conditions in the ocean. The application of chemostratigraphy to understanding and correlating the Neoproterozoic glaciations evokes considerable controversy, and many questions persist regarding the reliability and calibration of the δ 13 C, 87 Sr/ 86 Sr and δ 34 S record. Nevertheless, the individual glaciations appear to be characterized by distinct combined chemostratigraphic signatures, in large part due to the generally increasing strontium isotope composition of seawater through the Neoproterozoic Era.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".