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Record W3194523798 · doi:10.5194/gc-2021-18-rc2

Comment on gc-2021-18

2021· peer-review· en· W3194523798 on OpenAlexfundaboutno aff
Eric J. Pyle

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
FundersQueen's University
KeywordsPaceTRIPS architecturePhotogrammetryField (mathematics)Course (navigation)Computer scienceCompassRemote sensingData scienceEngineeringGeologyCartographyGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Geological (Engineering) Field Methods (GEOE/L 221) is a core course for two programs at Queen’s University in Kingston, Ontario, Canada where students learn foundational knowledge, skills, and methods to conduct field work that is used to investigate geological and geological engineering aspects of the Earth. Typically, this fall-term course involves weekly field trips in the Kingston area to visit a variety of rock outcrops to learn and practice methods of field navigation, observation, and measurement. Remote delivery of this course in fall 2020 due to COVID-19 without in-person field trips required a significant transformation, which included creating field and demonstration instructional videos, using 3D digital photogrammetry models of rock samples and outcrops, developing independent outdoor activities for pace and compass navigation, manual sketching and graphical measurements on paper, and utilizing a culminating immersive 3D video game style geological field mapping exercise. This paper examines these new course elements, how well the course learning objectives were achieved in a remote setting, and the successes and limitations of remote delivery. Although many new virtual elements enhance the course and should be incorporated to future offerings, a return to in-person field methods teaching for geological sciences and geological engineering courses is strongly recommended.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.351
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.3270.006

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 head0.071
GPT teacher head0.281
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes2
Has abstractyes

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