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Record W2804989478 · doi:10.1002/lol2.10077

Pipes or chimneys? For carbon cycling in small boreal lakes, precipitation matters most

2018· article· en· W2804989478 on OpenAlexaff
Heleen A. de Wit, Raoul‐Marie Couture, Leah Jackson‐Blake, Martyn N. Futter, Salar Valinia, Kari Austnes, José-Luis Guerrero, Yan Lin

Bibliographic record

VenueLimnology and Oceanography Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité Laval
FundersMiljødirektoratetNorges ForskningsrådNordForsk
KeywordsDissolved organic carbonTerrigenous sedimentEnvironmental sciencePrecipitationDeposition (geology)Hydrology (agriculture)Carbon cycleSedimentEnvironmental chemistryGeologyOceanographyEcosystemEcologyGeomorphologyChemistry

Abstract

fetched live from OpenAlex

Abstract Are small lakes passive pipes transporting terrigenous organic carbon (dissolved organic carbon [DOC]), or chimneys for CO 2 release in the landscape? Using a unique combination of 30‐yr measurements, sediment dating and modeling of a small humic lake and its catchment in southeast Norway, we calculated lateral DOC fluxes and in‐lake retention. Concentrations and fluxes rose significantly, driven by declining sulfur deposition and increased precipitation. In‐lake retention (% of inputs) declined because of higher discharge and lower residence times. DOC removal rates were not sensitive to residence time. Modeled in‐lake DOC removal was driven primarily by microbial metabolism and, secondarily, by flocculation, suggesting that the likely fate of lake‐retained DOC is CO 2 evasion to the atmosphere. Precipitation was the overriding landscape control on DOC fluxes and retention. In a wetter climate, small northern lakes will, on balance, function more as pipes than chimneys, with increasing lateral DOC fluxes but little change in CO 2 production.

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 score0.044
Threshold uncertainty score0.972

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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

Citations45
Published2018
Admission routes1
Has abstractyes

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