General considerations and highlights of low-lying coastal zones: passive continental margins from the poles to the tropics
Bibliographic record
Abstract
Abstract This Special Publication presents 23 papers that examine comparable, predominantly siliciclastic coastal zones of low-lying passive trailing-continental margins (primarily east Americas) from polar areas to the equator. The objective is to establish similarities and major differences among them. This introductory paper outlines major contributions of the various papers, but will also highlight coastal differences and briefly add information not fully treated by others. This is done in three parts: (a) some basic concepts are stressed, such as the importance of ‘coastal zone’ (total landscape) in the north–south comparison and the variable climates; (b) a review is made of the component materials of the coasts, such as difference in sediments owing to source rocks, weathering and geological history (glaciations), and in flora and fauna such as burrowing organisms; and (c) a few classical examples are reported from warm zones, such as Galveston Island and the Sapelo Island marshes, but the focus is on less well-known environments of cold areas – those most impacted by climate change. Each component of the coastal zone can develop diagnostic characteristics, but the entire assemblage of sedimentary and biological features is what uniquely defines present environments and allows identification of ancient coastal zones.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".