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Record W3018345035 · doi:10.1017/aog.2020.17

Carbon and nitrogen uptake rates and macromolecular compositions of bottom-ice algae and phytoplankton at Cambridge Bay in Dease Strait, Canada

2020· article· en· W3018345035 on OpenAlexafffundabout
Kwanwoo Kim, Sun‐Yong Ha, Bo Kyung Kim, C. J. Mundy, Kathleen Gough, Nicole Pogorzelec, Sang Heon Lee

Bibliographic record

VenueAnnals of Glaciology · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
FundersNational Research Foundation of KoreaKorea Polar Research InstituteNational Research FoundationNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaPolar Knowledge Canada
KeywordsPhytoplanktonAlgaeOceanographyBaySea iceArcticBiomass (ecology)Chlorophyll aEnvironmental scienceNutrientNitrogenGeologyEcologyChemistryBotanyBiology

Abstract

fetched live from OpenAlex

Abstract Our understanding of ice algal responses to the recent changes in Arctic sea ice is impeded by limited field observations. In the present study, environmental characteristics of the landfast sea-ice zone as well as primary production and macromolecular composition of ice algae and phytoplankton were studied in the Kitikmeot Sea near Cambridge Bay in spring 2017. Averaged total chlorophyll- a (Chl- a ) concentration was within the lower range reported previously for the same region, while daily carbon uptake rates of bottom-ice algae were significantly lower in this study than previously reported for the Arctic. Based on various indicators, the region's low nutrient concentrations appear to limit carbon uptake rates and associated accumulation of bottom-ice algal biomass. Furthermore, the lipids-dominant biochemical composition of bottom-ice algae suggests strong nutrient limitation relative to the distinctly different carbohydrates-dominant composition of phytoplankton. Together, the results confirm strong nitrate limitation of the local marine system.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.854

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations18
Published2020
Admission routes3
Has abstractyes

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