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Record W6931938168 · doi:10.5683/sp3/9my96f

Markinch Grain Elevator -- Markinch -- Metadata -- 2024

2024· dataset· en· W6931938168 on OpenAlexaff

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElevatorScannerLaser scanningMetadataLaser

Abstract

fetched live from OpenAlex

This dataset contains eighty-six (86) scans of the Markinch Grain Elevator and associated east and west annex, Markinch. All data was collected by the Capture2Preserv team between the 7th and 8th of June 2024. The Z+F 501X and the BLK 360 G1 laser scanners were both used to collected data of the elevator and associated annexes. The Z+F 5010X scanner was used to collect thirty-two (32) exterior scans of the grain elevator and east annex and eighteen (18) exterior scans of the west anned, which were registered in Z+F Laser Control and further processed in Autodesk ReCap Pro. The Leica BLK 360 G1 scanner was used to collect thirty-one (31) interior scans of the grain elevator and five (5) interior scans of the east annex, which were resgistered in Cyclone REGISTER 360 PLUS and further processed in Autodesk ReCap Pro. Data were exported to LAS in CloudCompare.

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)0.003
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.039
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0390.090

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 head0.032
GPT teacher head0.344
Teacher spread0.311 · 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
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

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