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Record W1678752961 · doi:10.1029/2008gb003297

The<i>p</i>CO<sub>2</sub>dynamics in lakes in the boreal region of northern Québec, Canada

2009· article· en· W1678752961 on OpenAlexaffabout
C. L. Roehm, Yves T. Prairie, Paul A. del Giorgio

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBorealEnvironmental scienceTrophic levelDissolved organic carbonLake ecosystemCo-occurrencePrecipitationDrainage basinEcologyWater qualityPhytoplanktonChlorophyll aTaigaPhysical geographyHydrology (agriculture)EcosystemNutrientGeologyGeographyChemistryBiology

Abstract

fetched live from OpenAlex

In this study, we examine the magnitude and temporal variability of surface water p CO 2 in a set of lakes in boreal Québec, and explore the links between lake and catchment properties. The study lakes were consistently supersaturated in CO 2 , with the mean lake p CO 2 ranging from 400 to over 1800 μ atm. There was significant interannual variability in p CO 2 , apparently driven by regional patterns in precipitation. The best multivariate model of average p CO 2 included dissolved organic carbon (DOC), lake area and chlorophyll as independent variables, suggesting that external carbon (C) loading to lakes plays a central role in lake CO 2 dynamics and that lake trophic status may modulate the influence of external C loading. We show that even if the key drivers of lake p CO 2 are similar, they interact differently among regions and the resulting models may be dramatically different. In particular, we show that although p CO 2 is invariably correlated to DOC, the shape of this relationship varies greatly among regions, suggesting large‐scale regional differences in C delivery, quality, and in‐lake processing. As a consequence, current models cannot be extrapolated across regions unless we apply region‐specific variables.

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)0.000
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 score0.076
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations105
Published2009
Admission routes2
Has abstractyes

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