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Record W2616944300 · doi:10.1080/20442041.2017.1296627

In the cold light of day: the potential importance of under-ice convective mixed layers to primary producers

2017· article· en· W2616944300 on OpenAlexafffund
Patricia Pernica, Rebecca L. North, Helen M. Baulch

Bibliographic record

VenueInland Waters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMixed layerConvective mixingEnvironmental scienceSnowPhytoplanktonAtmospheric sciencesConvectionTemperate climateSea iceOceanographyGeologyMeteorologyEcologyNutrientPhysicsBiology

Abstract

fetched live from OpenAlex

Temperate lakes are ice covered for much of the year; however, winter lake conditions have not been well studied and are undergoing rapid change. Using data collected during ice-on periods from 4 north-temperate water bodies, we report observations of stable surface layers, solar-induced convective mixed layers, and their potential impacts on phytoplankton. The convective mixed layer is defined as the region where the convective Richardson number (Ri) is ≤1. In the absence of a convective mixed layer, peaks in chlorophyll a were near the ice–water interface. Light conditions here seemed sufficient to support phytoplankton biomass accrual in the short-term in 50% of our measurements, although snow depths >13.5 cm may lead to light limitation. When a convective mixed layer was present, light conditions were sufficient for biomass accrual in 37.5% of cases. The frictional timescale for damping averaged 15 minutes, indicative of a lack of mixing at night. Convective mixing depths and velocity increased as snow declined, and results demonstrated the potential for rapid convective mixed layer deepening (up to 6.6 m h−1), underscoring the highly dynamic physical environment under ice. Although declining periods of ice cover have been subject to much attention, changes in snow cover may have equally important implications for primary producers and the potential for under-ice blooms. This link between physics and biology must be further explored to better understand how changing winters will affect water bodies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.284

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 science0.0000.000
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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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

Citations70
Published2017
Admission routes2
Has abstractyes

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