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Record W3167182207 · doi:10.1088/1748-9326/ac0be3

The role of lake morphometry in modulating surface water carbon concentrations in boreal lakes

2021· article· en· W3167182207 on OpenAlexafffundabout
Joan Pere Casas‐Ruiz, Julia Jakobsson, Paul A. del Giorgio

Bibliographic record

VenueEnvironmental Research Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiogeochemistryBorealEnvironmental scienceCarbon fibersShorePhysical geographyCarbon cycleEcologyHydrology (agriculture)GeologyOceanographyEcosystemGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Earth’s lakes vary greatly in size and morphometry, from small circular lakes of hundreds of m 2 to large and deep fractal systems of several thousands of km 2 . Previous research has demonstrated a link between the size of lakes and their carbon dynamics. However, the influence of lake morphometry on lake carbon biogeochemistry remains largely unexplored. Here, we analyze the morphometry and carbon concentrations of more than 250 lakes across boreal Quebec, encompassing a wide range in lake size from 0.002 to 4300 km 2 . We show that, in addition to lake size, the biogeochemistry of lake carbon is influenced by the circularity, shoreline complexity and vertical profile of the lake. Yet the type and degree of influence vary among the different carbon species. A comparative exercise shows that taking into account the morphometry of lakes moderately increases the predictive power of empirical models of carbon concentration across lakes. Therefore, future studies might benefit from adding lake morphometry metrics to the empirical rules used for prediction and upscaling.

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 score0.074
Threshold uncertainty score0.937

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations30
Published2021
Admission routes3
Has abstractyes

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