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Record W6929275927 · doi:10.48512/xcv8488768

Class III: Intensive Cultural Resources Inventory of up to 212 Acres, Cavalier Space Force Station, Pembina County, North Dakota

2022· article· en· W6929275927 on OpenAlexaboutno aff

Bibliographic record

VenueThe Digital Archeological Record (tDAR) · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsKnightQuarter (Canadian coin)Resource (disambiguation)Boundary (topology)Class (philosophy)Geological surveySpace (punctuation)

Abstract

fetched live from OpenAlex

Stell Environmental Enterprises, Inc. (Stell) performed a Class III: Cultural Resources Inventory of 196.89 acres (ac) managed by Cavalier Space Force Station (SFS) under subcontract agreement 1F-60506 with Argonne National Laboratories (Argonne). Argonne is the prime contractor under contract project EGYNA53201118. Prior to field work, Stell personnel visited the State Historical Society of North Dakota and performed a Class I: Literature Search of the Cavalier SFS and 1- mile buffer of the surrounding area. The literature search identified a total of eight cultural resources surveys conducted within the 1-mile buffer in and around the Cavalier SFS. These surveys resulted in the identification of five cultural resources consisting of one prehistoric lithic scatter, one historical homestead, and three historic architectural sites. The single cultural resource located within the installation boundary is the Cavalier SFS Perimeter Acquisition Radar (Site). All relevant information was incorporated into a research design that provided guidance for performing the cultural resources inventory under this contract. A pedestrian surface survey of the project area did not identify any cultural resources. Due to the project area being larger than 40 ac, Stell’s field efforts and results are presented in this full report.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.220
Teacher spread0.195 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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