Comment on Non‐unique stratal geometries: implications for sequence stratigraphic interpretations, by: P.M. Burgess and G.D. Prince,<i>Basin Research</i>(2015) 27, 351–365
Bibliographic record
Abstract
Abstract The non‐unique variability highlighted by Burgess & Prince (Basin Res. 2015,27, 351) (i.e. the origin and timing of maximum flooding surfaces, maximum regressive surfaces and subaerial unconformities; the process of topset aggradation in relation with the various types of shoreline trajectory; and the multiple controls that may affect the progradation and retrogradation of a shoreline) is irrelevant to the workflow of sequence stratigraphy. What is relevant is the observation of the unique stratal geometries that are diagnostic to the definition of all units and surfaces of sequence stratigraphy. In downstream‐controlled settings, these unique stratal stacking patterns relate to the forced regressive, normal regressive and transgressive shoreline trajectories. Multiple controls interplay during the formation of each type of stacking pattern, including accommodation, sediment supply and the energy of the sediment‐transport agents. This interplay explains the non‐unique variability, but does not change the unique criteria that afford a consistent application of sequence stratigraphy. Failure to rationalize the non‐unique variability within the context of unique stratal geometries is counterproductive, and obscures the simple workflow of sequence stratigraphy.
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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".