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Record W4283792177 · doi:10.1038/s41597-022-01434-y

An 8-year record of phytoplankton productivity and nutrient distributions from surface waters of Saanich Inlet

2022· article· en· W4283792177 on OpenAlexafffundabout
Sile M. Kafrissen, Karina E. Giesbrecht, Brandon J. McNabb, Jennifer E Long, Curtis W. Martin, Shea N. Wyatt, Marcos Lagunas, Diana E. Varela

Bibliographic record

VenueScientific Data · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaUniversity of Victoria
KeywordsPhytoplanktonInletNutrientProductivityEnvironmental scienceOceanographyPrimary productivityEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Phytoplankton are the base of nearly all marine food webs and mediate the interactions of biotic and abiotic components in marine systems. Understanding the spatial and temporal changes in phytoplankton growth requires comprehensive biological, physical, and chemical information. Long-term datasets are an invaluable tool to study these changes, but they are rare and often include only a small set of measurements. Here, we present biological, physical and chemical oceanographic data measured periodically between March 2010 and November 2017 from the euphotic zone of Saanich Inlet, a temperate fjord on the west coast of British Columbia, Canada. The dataset includes measurements of dissolved macronutrients, total and size-fractionated chlorophyll-a, particulate carbon, nitrogen and biogenic silica, and carbon and nitrate uptake rates. This collection describes phytoplankton dynamics and the distribution of biologically-available macronutrients over time in the upper water column of Saanich Inlet. We establish a baseline for future investigations in Saanich Inlet and provide a data collection protocol that can be applied to similar productive coastal regions.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.040
GPT teacher head0.281
Teacher spread0.241 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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 routes3
Has abstractyes

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