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Record W2406569446 · doi:10.1139/as-2015-0026

Influence of Hudson Bay on the carbon dynamics of a Hudson Bay Lowlands coastal site

2016· article· en· W2406569446 on OpenAlexafffundvenueabout
Kristina Kirilova Delidjakova, Richard Bello, Kaz Higuchi, Bipin Pokharel

Bibliographic record

VenueArctic Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsYork University
FundersAboriginal Affairs and Northern Development CanadaChurchill Northern Studies CentreYork UniversityArcticNet
KeywordsBayEnvironmental scienceSubmarine pipelineAdvectionEddy covariancePhotosynthetically active radiationEcosystem respirationPlateau (mathematics)OceanographyEcosystemAtmospheric sciencesClimatologyGeologyPhotosynthesisEcology

Abstract

fetched live from OpenAlex

Eddy covariance (EC) estimates of net ecosystem exchange (NEE) and the surface energy balance were gathered from an elevated peat plateau within the Hudson Bay Lowlands near Churchill, Manitoba, Canada (58°43′46″N, 93°49′57″W) during the growing season of 2007. Data were segregated into onshore and offshore wind regimes to assess the advective influence of the generally cold and moist Hudson Bay air masses compared to generally warm and dry air masses of nonmarine origin. Monthly average NEE ranged from an uptake of 0.2 µmol·m −2 ·s −1 in September to 5.6 µmol·m −2 ·s −1 in July. Over the growing season, onshore winds from Hudson Bay contributed to an average 4.2 °C reduction in air temperature and an NEE increase of 27%. When normalized with respect to sunlight receipt, the ratio of gross primary production to photosynthetically active radiation (GPP/PAR) was 26% stronger for offshore regimes than for onshore, while the ratio of ecosystem respiration to PAR (ER/PAR) was 71% stronger for offshore regimes. It was concluded that GPP maintains the same strength for both wind regimes, while ER is significantly stronger for offshore regimes, resulting in reduced NEE capacity during periods when winds originate from inland.

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 score0.051
Threshold uncertainty score0.682

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Citations8
Published2016
Admission routes4
Has abstractyes

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