MétaCan
Menu
Back to cohort
Record W6889645317 · doi:10.26071/ogsl-abd9c822-1ade

Exploring Nitrogen Cycling in Oxygen-Depleted Oceans: Description From Estuary and Gulf of St. Lawrence

2024· dataset· en· W6889645317 on OpenAlexaff

Bibliographic record

VenueOGSL repository · 2024
Typedataset
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsDalhousie UniversityUniversité du Québec à Rimouski
Fundersnot available
KeywordsEstuaryNitrogenNitrateSedimentEcosystemNitrogen cycleCyclingHydrology (agriculture)Water quality

Abstract

fetched live from OpenAlex

This dataset covers an in-depth study of the nitrogen cycle along the oxygen concentration gradient of the Laurentian Channel in the St. Lawrence Estuary and Gulf. The study, conducted during the summers of 2021 and 2022, aimed to assess the impacts of deoxygenation on the marine ecosystems of this region. To do this, sampling was carried out aboard the Research Vessel Coriolis II. Water samples allowed the measurement of nutrient and dissolved gas concentrations, including N2, Ar and N2O, in the water column. Sediment samples were also collected to perform incubations and characterize pore waters, with the aim of evaluating the contribution of sediments to N2 production and nitrate consumption. The results obtained provide significant insight into the processes of fixed nitrogen loss and changes in stoichiometric ratios induced by deoxygenation in the waters of the St. Lawrence Estuary and Gulf. This study thus allows for a better understanding of the impacts of this phenomenon on the nitrogen cycle and the functioning of these marine ecosystems.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.821
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.006

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 head0.048
GPT teacher head0.263
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 designObservational
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

Explore more

Same venueOGSL repositorySame topicAdvanced Vision and ImagingFrench-language works237,207