MétaCan
Menu
Back to cohort
Record W1976878377 · doi:10.2118/161015-pa

Atomic-Force Microscopy: A New Tool for Gas-Shale Characterization

2012· article· en· W1976878377 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCharacterization (materials science)Atomic force microscopyKerogenHydraulic fracturingOil shaleNanotechnologyNanoporeMaterials sciencePetroleum engineeringGeologyMineralogySource rock

Abstract

fetched live from OpenAlex

Summary An atomic-force microscope (AFM), a relatively new tool for studying surface characterization, can generate image features down to atomic resolution. Not only can the AFM obtain topographic images of surfaces, but it also can simultaneously identify different materials on a surface at high resolution. Since its invention in the 1980s, AFM has been used in material science and medical research, although it has not received the attention that it probably deserves in reservoir engineering. The emergence of unconventional shale-gas reservoirs, however, has opened new research frontiers for the AFM in the field of reservoir engineering. The unique capabilities of the AFM make it ideal for studying nanopores, organic materials (kerogen), minerals, and diagenetic fractures in shales. It also can be used to measure localized bulk modulus of elasticity on a surface for further implications in geophysical exploration and designing hydraulic fracturing. We introduce different AFM techniques for all these applications, along with example results.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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