Bibliographic record
Abstract
Abstract The papers contained on this CD mostly originate from a session at the 2002 Annual Meeting of the Canadian Society of Petroleum Geologists, repeated at the 2004 Dallas AAPG Meeting. The theme of both sessions was “Facies Models Revisited”, to see what sort of progress had been made since the third (1992) edition of Facies Models, published by the Geological Society of Canada. During the ten years between 1992 and 2002, there has been considerable progress in the understanding of modern and ancient depositional environments. This additional complexity makes modeling much more difficult, and raises the problem of whether modeling still serves a purpose. The original reasons for creating facies models still exist—a model is a point of comparison, it is a guide for further observations, it serves as a basis for hydrodynamic interpretation, and most importantly, it acts as a predictor in new situations. Using submarine fans as an example, it is clear that progress during the last ten years (particularly in 3-D seismic) has highlighted the inadequacy of all pre-existing models—indeed, no comprehensive models have been proposed since the mid eighties. Yet with continued and increasing exploration in submarine fan systems, predictive models are even more necessary. The traditional approach, of distilling the features that modern and ancient systems have in common, is extremely difficult (and probably naive) in such diverse and complex systems. Instead, it is necessary to identify all of the constituent building blocks of submarine fans (channels, point bars, levees, splays, frontal lobes and so on), and try to identify the salient features of each. New models for particular situations can be constructed by examining the relationships of the constituent building blocks. For example, sinuous channels, levees and splays may be closely related in space, whereas frontal lobes are unlikely to be related to sinuous leveed channels (except for the channel that ultimately feeds the lobe). A three-dimensional reconstruction can therefore be made by examining the building blocks that are closely and commonly related, and also using information from the building blocks that are seldom or never found in juxtaposition. These principles, discussed above for submarine fans, can be applied to all depositional environments, at all scales. The ideas are elaborated in this introductory paper, and can be seen in the other contributions to this CD.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".