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Record W2258601458 · doi:10.1080/11956860.2015.1047137

Dissolved organic matter concentration, optical parameters and attenuation of solar radiation in high-latitude lakes across three vegetation zones

2015· article· en· W2258601458 on OpenAlexaffvenue
Laura Forsström, Milla Rautio, Mathieu Cusson, Sanna Sorvari, Raino-Lars Albert, Michio Kumagai, Atte Korhola

Bibliographic record

VenueEcoscience · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsTundraDissolved organic carbonEnvironmental scienceLatitudePhotosynthetically active radiationAtmospheric sciencesArcticOceanographyGeographyGeologyPhotosynthesisBotanyBiology

Abstract

fetched live from OpenAlex

ABSTRACT High-latitude lakes are usually transparent, due to their low productivity and low concentration of dissolved organic matter (DOM), but large variations in lake optical properties can be found within and between regions. We investigated the light regimes in relation to DOM in 18 oligotrophic, high-latitude lakes across mountain birch woodland, shrub tundra and barren tundra in north-west Finnish Lapland. In 12 lakes >1% of photosynthetically active radiation (PAR) reached the lake bottom, while 1% UV-B depth ranged from 0.1 to >12 m. Lakes located in barren tundra had highest transparency, lowest dissolved organic carbon (DOC) concentration and lowest DOM absorption (a440) (mean values: Kd PAR 0.3m-1, DOC 2.1mg l-1, a440 0.4m-1), while lakes in shrub tundra and mountain birch forest were less transparent (DOC 4.7 mg l-1, a440 1.4 m-1). Solar attenuation and lake transparency was best explained by a440. Our survey emphasizes the importance of catchment type on DOM characteristics and lake optics. We predict that even small changes in DOM quality may largely change the UV radiation exposure of lakes while changes in PAR may have smaller biological effects in these shallow lakes that are already illuminated to the bottom.

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 score0.084
Threshold uncertainty score0.982

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 communication0.0000.001
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 head0.014
GPT teacher head0.217
Teacher spread0.202 · 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

Citations28
Published2015
Admission routes2
Has abstractyes

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