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First assessment of methane and carbon dioxide emissions from shallow and deep zones of boreal reservoirs upon ice break‐up

2006· article· en· W2159696725 on OpenAlexaffabout
Éric Duchemin, Marc Lucotte, René Canuel, Nicolas Soumis

Bibliographic record

VenueLakes & Reservoirs Science Policy and Management for Sustainable Use · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEnvironmental scienceBorealGreenhouse gasCarbon dioxideMethaneAtmospheric sciencesHydroelectricityClimate changeClimatologyHydrology (agriculture)OceanographyGeologyChemistryEcology

Abstract

fetched live from OpenAlex

Abstract Most studies dealing with greenhouse gas (GHG) emissions from large boreal reservoirs were conducted during the ice‐free period. In this paper, the potential methane (CH4) and carbon dioxide emissions are estimated for two hydroelectric reservoirs, as well as for a small experimental reservoir from boreal latitudes (northern Quebec, Canada) at the ice break‐up event through diffusion (diffusive fluxes) and release of bubbles (bubbling fluxes). The results of this preliminary study suggest that the winter diffusive fluxes at the air–water interface of the sampled reservoirs represent < 7% of their cumulative carbon emissions during the ice‐free period. Furthermore, the release upon ice‐break of CH4 bubbles accumulated under the ice cover during the winter could represent 2% of the summer carbon emissions from hydroelectric reservoirs in northern Quebec. The results presented herein suggest that the GHG emissions upon ice break‐up from the boreal reservoirs investigated are a small, but non‐negligible, component of their annual GHG emissions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.254
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2006
Admission routes2
Has abstractyes

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