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Record W4309607336 · doi:10.3389/fmars.2022.942799

Evidence of phytoplankton blooms under Antarctic sea ice

2022· article· en· W4309607336 on OpenAlexaff
Christopher Horvat, Kelsey Bisson, Sarah Seabrook, Antonia Cristi, Lisa Matthes

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité Laval
FundersGoddard Space Flight CenterSchmidt Family FoundationNational Institute of Water and Atmospheric Research
KeywordsSea iceOceanographyPhytoplanktonEnvironmental scienceArctic ice packArcticSpring bloomCryosphereBloomArgoDrift iceArctic sea ice declineClimatologyGeologyEcologyNutrient

Abstract

fetched live from OpenAlex

Areas covered in compact sea ice were often assumed to prohibit upper-ocean photosynthesis. Yet, under-ice phytoplankton blooms (UIBs) have increasingly been observed in the Arctic, driven by anthropogenic changes to the optical properties of Arctic sea ice. Here, we show evidence that the Southern Ocean may also support widespread UIBs. We compile 77 time series of water column samples from biogeochemical Argo floats that profiled under compact (80%–100% concentration) sea ice in austral spring–summer since 2014. We find that that nearly all (88%) such measurements recorded increasing phytoplankton biomass before the seasonal retreat of sea ice. A significant fraction (26%) met a observationally determined threshold for an under-ice bloom, with an average maximum chlorophyll-a measurement of 1.13 mg/m 3 . We perform a supporting analysis of joint light, sea ice, and ocean conditions from ICESat-2 laser altimetry and climate model contributions to CMIP6, finding that from 3 to 5 million square kilometers of the compact-ice-covered Southern Ocean has sufficient conditions to support light-limited UIBs. Comparisons between the frequency of bloom observations and modeled bloom predictions invite future work into mechanisms sustaining or limiting under-ice phytoplankton blooms in the Southern Hemisphere.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.221
Teacher spread0.207 · 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 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

Citations34
Published2022
Admission routes1
Has abstractyes

Explore more

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