A Review and Reassessment of Travertine Classification
Bibliographic record
Abstract
This paper provides a review of the classification of travertines with emphasis on their morphology. Three criteria are used to describe them: geochemistry, microfabric and morphology. Geochemically, travertines may be divided into two groups, the meteogene travertines, where the carrier carbon dioxide originates in the soil and epigean atmosphere, and the thermal (thermogene) travertines where the carbon dioxide comes from thermally generated sources. Many travertine fabrics are influenced by bacteria and plants. These include 'stromatolitic' forms, many oncoids, shrubs, tufts, mats and moss travertines. Morphologically, travertines are conveniently divided into autochthonous (spring mounds and ridges, cascades, barrages, fluvial and lacustrine crusts, paludal deposits and cemented rudites) and the allochthonous or clastic travertines (valley-fills, back-barrage deposits, alluvial cones). Travertine deposits often include a wide range of fabrics and morphologies in one system. They are influenced locally by discharge, slope, vegetation, climate and human activity. Intergradations occur, both within travertine types but also with other freshwater deposits, e.g. calcrete and lake chalk. The influence of travertine deposition on the local hydrology and geomorphology is also discussed. The review emphasises the significance of scale and hydrology and aims to provide a unified scheme of travertine classification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".