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Record W6944811869 · doi:10.18739/a2pz51n58

Conductivity-Temperature-Depth (CTD) and Geochemistry Data from 2021 Expedition of the Canadian Coast Guard Ship Louis S. St-Laurent

2022· dataset· en· W6944811869 on OpenAlexaboutno aff

Bibliographic record

VenueUC Santa Barbara · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoast guardCruiseHydrographyBeaufort scaleOcean gyreDocumentation

Abstract

fetched live from OpenAlex

Contains data from a hydrographic cruise conducted about the aboard the Canadian Coast Guard Ship, CCGS Louis S. St-Laurent, August 19 to September 16th, 2021 under the Beaufort Gyre Observing System (BGOS)/Joint Ocean Ice Study (JOIS). There were 57 conductivity temperature depth (CTD) casts performed using a SBE911+ CTD system with 24 10-Liter (L) Niskins. The CTD data for down casts are provided in 1-decibar (db) averaged files (*.cnv), in Seabird's text format with one file per cast and separate files. Cruise documentation is available as a text file. Geochemistry data are provided in a separate excel spreadsheet.

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 score0.646
Threshold uncertainty score0.705

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.042

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 head0.059
GPT teacher head0.275
Teacher spread0.216 · 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
Published2022
Admission routes1
Has abstractyes

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