MétaCan
Menu
Back to cohort
Record W6889713305 · doi:10.25921/g555-9q86

Surface underway pH, water temperature, salinity, dissolved oxygen, chlorophyll and discrete analyses of dissolved inorganic carbon and total alkalinity from the underway TSG collected during the RRS Discovery fall 2023 oceanographic survey cruise (EXPOCODE 32V320231006) in the North Atlantic Ocean and Labrador Sea from 2023-10-06 to 2023-10-26 (NCEI Accession 0299020)

2024· dataset· en· W6889713305 on OpenAlexaffabout

Bibliographic record

VenueNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) · 2024
Typedataset
Languageen
FieldComputer Science
TopicImbalanced Data Classification Techniques
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsDissolved organic carbonAlkalinitySampling (signal processing)Water columnChlorophyll aPlanktonSurface waterTotal inorganic carbonTotal organic carbon

Abstract

fetched live from OpenAlex

This dataset includes underway sensor measurements collected during the RRS Discovery Fisheries and Oceans Canada (DFO) Atlantic Zone Monitoring Program (AZMP) cruise (EXPOCODE 32V320231006) in the North Atlantic Ocean and Labrador Sea from 2023-10-06 to 2023-10-26. These data include water temperature, salinity, dissolved oxygen, and chlorophyll a fluorescence. In addition, daily sampling for measurement of DIC and TA were conducted. Oceanographic sampling of physical, biological, and chemical parameters is performed annually along selected fixed sections as part of the AZMP. The full sampling program consists at a minimum of a vertical profile of the entire water column (temperature; salinity; dissolved oxygen); rosette bottle casts at selected depths (nutrients; DIC and TA; and other biological parameters) as well as plankton net tows.

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 score0.976
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.036

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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 routes2
Has abstractyes

Explore more

Same venueNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI)Same topicImbalanced Data Classification TechniquesFrench-language works237,207