MétaCan
Menu
Back to cohort
Record W2805290364 · doi:10.14430/arctic4716

Seasonal Variations in the Limnology of Noell Lake in the Western Canadian Arctic Tracked by In Situ Observation Systems + Supplementary Appendix 1 (See Article Tools)

2018· article· en· W2805290364 on OpenAlexvenueaboutno aff
Benjamin Angus Paquette-Struger, Frederick J. Wrona, David Atkinson, Peter di Cenzo

Bibliographic record

VenueARCTIC · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLimnologyEnvironmental scienceOceanographyStratification (seeds)ArcticHydrology (agriculture)Lake ecosystemHypolimnionBiogeochemical cycleWater columnShelf icePhysical geographyClimatologyEcosystemGeologyGeographyEcologyEutrophicationArctic ice pack

Abstract

fetched live from OpenAlex

Research investigating climate-driven changes in northern lake ecosystems is complicated by a legacy of initiatives that have used sporadic observations, often confined to open-water seasons, to define the lake state. These observations have conventionally been lake water samples analyzed for a suite of physical and chemical parameters and are indicative of only the days or hours immediately before sampling. Monitoring approaches that sample a broader scope of limnological parameters over a continuous period are needed to augment existing strategies. A study of the seasonal changes to limnological parameters in Noell Lake was performed by analyzing continuous, hourly data collected from a series of automated and non-automated moorings over the period July 2012 to July 2013. Noell Lake was found to be strongly stratified throughout the open-water and under-ice seasons, with two prominent mixing periods in spring and fall. Processes of cryoconcentration and respiration intensified density-driven stratification while the lake is ice-covered, with the deep holes of Noell Lake becoming particularly saline and oxygen-depleted all year. Hypoxia was prevalent during the under-ice season because these physical and biogeochemical processes eliminated mixing from the lower lake depths while oxygen demand remained high. Use of continuous hourly monitoring facilitated improved understanding of the dynamical response of Noell Lake to atmospheric forcing.

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 score0.431
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.219
Teacher spread0.199 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueARCTICSame topicArctic and Antarctic ice dynamicsFrench-language works237,207