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Record W2981973638 · doi:10.4095/288045

Soil auger and slide-hammer core sampling

2011· report· en· W2981973638 on OpenAlexaff
S A Wolfe

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsAugerHammerCore (optical fiber)Sampling (signal processing)GeologyMaterials scienceArchaeologyMetallurgyPhysicsGeographyComposite materialOptics

Abstract

fetched live from OpenAlex

After the shovel, hand auger probably represents the simplest of light-weight shallow coring methods available. GCS Northern has a T-handle soil auger, with 4-inch diameter auger heads applicable for loam soils, sand and clay, and extension rods for coring in excess of 5 metres. Experience in a range of soils has shown that the open-faced clay auger head is the ideal all-purpose auger, which permits easy sampling and removal of soil from the auger. A soil auger is ideal in most moist soils (particularly sandy soils), where exposures are absent. The auger can penetrate wet and even saturated silts and clays, permitting sampling several metres below the watertable in some circumstances. It is a good alternative where vibra-coring is not possible. The auger can be operated by one person, with quick-connect (used by GSC) or threaded options. It is very portable, and typical coring depths reach 3 to 5 metres with excellent core recovery. Disadvantages include recovery of only disturbed samples, and difficulty in recovering very dry or very wet, non-cohesive samples. Most of these disadvantages may be overcome by utilizing a slide-hammer soil core sampler. GSC Northern has experience with a 2-inch diameter, 12-inch long sampler. The short undisturbed samples are ideal for optical dating. The sampler can use an existing auger-hole to obtain a core sample, and uses a slide-hammer for coring, and T-handle for extraction. The ideal method is to combine auger sampling and slide-hammer coring. A disadvantage is that it can be difficult to extract the sample from the corer. For this reason a split-tube sampler is available, but has not been tried by GSC personnel.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.009

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 head0.096
GPT teacher head0.278
Teacher spread0.182 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2011
Admission routes1
Has abstractyes

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