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Record W1606931280 · doi:10.7202/1000322ar

Pingos of the Tuktoyaktuk Peninsula Area, Northwest Territories

2011· article· en· W1606931280 on OpenAlexaffvenueabout
Jenny Mackay

Bibliographic record

VenueGéographie physique et Quaternaire · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of British Columbia
FundersUniversity of WashingtonU.S. Department of Energy
KeywordsPermafrostGeologyAggradationLevellingGeomorphologyHydrology (agriculture)Geotechnical engineeringStructural basinOceanography

Abstract

fetched live from OpenAlex

Most pingos have grown in residual ponds left behind by rapid lake drainage through erosion of ice-wedge polygon systems. The field studies (1969-78) have involved precise levelling of numerous bench marks, extensive drilling, detailed temperature measurements, installation of water pressure transducers below permafrost and water (ice) quality, soil, and many other analyses. Precise surveys have been carried out on 17 pingos for periods ranging from 3 to 9 years. The field results show that permafrost aggradation in saturated lake bottom sediments creates the high pore water pressures necessary for pingo growth. The subpermafrost water pressures frequently approach that of the total litho-static pressure of permafrost surrounding a pingo. The water pressure is often great enough to lift a pingo and intrude a sub-pingo water lens beneath it. The basal diameter of a pingo is established in early youth after which time the pingo tends to grow higher, rather than both higher and wider. The shutoff direction of freezing is from periphery to center. When growing pingos have both through going taliks and also permeable sediments at depth, water may be expelled downwards by pore water expulsion from freezing and consolidation from self loading on saturated sediments. Pingos can rupture from bursting of the sub-pingo water lens. Otherwise, pingo failure is at the top and periphery. Hydraulic fracturing is probably important in some pingo failures. Water loss from sub-pingo water lenses causes subsidence with the subsidence pattern being the mirror image of the growth pattern; i.e. greatest subsidence at the top. Small peripheral bulges may result from subsidence. Old pingos collapse from exposure of the ice core to melting by overburden rupture, by mass wasting, and by permafrost creep of the sides.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.226
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations164
Published2011
Admission routes3
Has abstractyes

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