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Record W4283164124 · doi:10.5194/icg2022-71

The singularity of hypogene karst geomorphology

2022· preprint· en· W4283164124 on OpenAlexaboutno aff
Alexander Klimchouk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicKarst Systems and Hydrogeology
Canadian institutionsnot available
Fundersnot available
KeywordsKarstGeologyGeomorphologyAquiferDenudationGroundwater rechargeGeochemistryHypogeneHydrology (agriculture)GroundwaterTectonicsPaleontologyGeotechnical engineering

Abstract

fetched live from OpenAlex

<p>Geomorphology of exposed epigene karst (EK) is determined by meteoric recharge from the surface, which creates characteristic solution landforms (karren, dolines, ponors, poljes, uvalas, etc.) conveying surface runoff to the conduit systems. In covered EK, collapse and subsidence forms are also common.  On a larger scale, EK favors the formation of plateau-topped massifs as the internal drainage diminishes fluvial erosion.</p><p>In contrast, hypogene karst (HK) is driven by rising flow from underlying aquifers or cross-formational fracture/conduit systems recharged from deep-seated sources and/or distant areas. It commonly develops without direct relationships with the surface. In the <em>artesian type of HK</em> (in confined multistory aquifer systems under a gravity-driven circulation regime), karstification is supported by upward leakages between aquifers in zones of piezometric lows, generally corresponding to topographic lows. Thus, there is an oblique relationship between landscape and karst systems in this type of HK. In the <em>endogenous type of HK</em>, solution cavities are commonly associated with cross-formational fluid-conducting fracture zones (fracture-vein fluid systems).  Hypogene caves are documented at depths up to 8 km (Tarim basin, China). With uplift and denudation, hypogenically karstified units are brought into the shallow subsurface and karst systems start receiving the surface expression. Assuming a long-term denudation rate at 1000 mm/kyr (the order typical for orogenic areas), a karstified unit at depth of 2 km can receive the surface exposure since the beginning of the neotectonic period (ca. 2 myr).</p><p>Landscape features of HK are peculiar and may occur as isolated forms but often as characteristic complexes that are commonly distributed by clusters. In hydrogeologically active HK, it is first expressed as rising flow features (vent orifices and mineral or clastic mounds). Outstanding examples are hydrothermal springs in Yellowston, USA and Lusi mud volcano in Phillipines.  Further expression of HK systems occurs as collapse sinkholes (shafts) forming over substantial solution cavities or breccias pipes. Examples are numerous from areas with varying geodynamic settings (Western Canadian Basin; Konya Basin, Turkey; Dead Sea area, Israel; Zacaton area, Mexico; Gambier volcano area, Australia). Rift conduits, when intercepted by the surface, give rise to «earth fractures» and slot canyons (Colorado Plateau; South Africa). Where intervals with extensive stratiform hypogene caves are brought to the shallow subsurface, unroofing and decomposition of these caves causes the formation of dense sinkhole fields and «rock cities». With further uplift, cliffs are often formed along karst rifts, displaying characteristic solution features of rising flow in walls.</p><p>Three complexes of landscape HK features can be identified, often representing successive stages of the geomorphological evolution: (1) <em>rising discharge complex</em> - vent orifices and mineral or clastic mounds; (2) <em>areal exhumation complex</em> - collapse sinkholes and shafts, subsidence depressions, cave unroofing features (dense sinkhole fields, rock cities), rock arches and windows, earth fractures, slot canyons, exhumation of mineralized features (sandstone, silica, and carbonate chimneys and mounds); (3) <em>cliff complex</em> - rock shelters, amphitheaters, tafoni, honeycomb surfaces, cavernous zones, half-tubes in the scarps. The geomorphology of HK is distinctly different from EK geomorphology by both, characteristic feature assemblages and evolution tracks.</p>

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.000

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.

Opus teacher head0.019
GPT teacher head0.223
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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