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Record W6927154417 · doi:10.26071/ogsl-969715ba-3747

Chlorophyll-a and Salinity Concentrations Derived from Satellite Images for the Estuary and Gulf of St. Lawrence (1998-2023)

2022· dataset· en· W6927154417 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOGSL repository · 2022
Typedataset
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsnot available
Fundersnot available
KeywordsEstuarySalinitySatelliteBaseline (sea)Ocean colorChlorophyll a

Abstract

fetched live from OpenAlex

This dataset presents the result of a model developed to retrieve chlorophyll-a (chla) and salinity concentrations from various satellites in the St. Lawrence Estuary and Gulf. This version replaces the previous ones, with estimates derived from a retuning of the model based on additional in situ and satellite-derived data. The input data is now exclusively from the European Space Agency's Ocean Color Climate Change Initiative (CCI) program. The ocean color, seen from space, makes it possible to estimate the chlorophyll content in water. This pigment is an index of the biomass of microscopic algae. Compared to the Atlantic Ocean, the waters of the Estuary and Gulf of St. Lawrence are rather isolated and are highly mixed with fresh water from numerous rivers. This dataset is produced as part of the Coastal Environmental Baseline Program under Fisheries and Oceans Canada's Oceans Protection Plan.

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.

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 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 score0.107
Threshold uncertainty score0.817

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 science0.0000.000
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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