Glaciogenic reservoirs and hydrocarbon systems: an introduction
Bibliographic record
Abstract
Abstract Glaciogenic reservoirs host important hydrocarbon and groundwater resources across the globe. Their complexity and importance for exploration and palaeoclimate reconstruction have made glaciogenic successions popular subjects for study. In this paper we provide an overview of the palaeoclimatic and tectonic setting for Earth glaciation and a chronological account of glaciogenic deposits sincec.750 Ma, with particular emphasis on their reservoir potential and associated hydrocarbon systems. Hydrocarbon accumulations within glaciogenic reservoirs occur principally in Palaeozoic (Late Ordovician and Permo-Carboniferous) sandstones in South America, Australia, North Africa and the Middle East, with relatively minor occurrences of shallow gas hosted in Pleistocene deposits in the North Sea and Canada. Groundwater reserves occur within glaciogenic sandstones across the northern European lowland and in North America. The main glaciogenic environments range from subglacial to glacier front to proglacial and deglacial. Rapidly changing environments, hydrodynamic regimes and glacier-front and subglacial deformation often result in very complex glaciogenic sequences with significant challenges for reconstruction of their origin and resource importance, which this volume seeks to address.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".