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Record W2922299339 · doi:10.1007/s00027-019-0631-6

Environmental conditions for phytoplankton influenced carbon dynamics in boreal lakes

2019· article· en· W2922299339 on OpenAlexfundno aff
Fabian Engel, Stina Drakare, Gesa A. Weyhenmeyer

Bibliographic record

VenueAquatic Sciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersSveriges LantbruksuniversitetVetenskapsrådetNaturvårdsverketGlobal Lake Ecological Observatory NetworkKnut och Alice Wallenbergs StiftelseEuropean CommissionH2020 Marie Skłodowska-Curie ActionsHavs- och Vattenmyndigheten
KeywordsPhytoplanktonEutrophicationEnvironmental scienceBorealOceanographyTotal organic carbonDissolved organic carbonNutrientCarbon cycleEnvironmental chemistryHydrology (agriculture)EcologyEcosystemGeologyChemistryBiology

Abstract

fetched live from OpenAlex

The partial pressure of CO 2 ( p CO 2 ) in lake water, and thus CO 2 emissions from lakes are controlled by hydrologic inorganic carbon inputs into lakes, and in-lake carbon transformation (mainly organic carbon mineralization and CO 2 uptake by primary producers). In boreal lakes, CO 2 uptake by phytoplankton is often considered to be of minor importance. At present, however, it is not known in which and how many boreal lakes phytoplankton CO 2 uptake has a sizeable influence on the lake water p CO 2 . Using water physico-chemical and phytoplankton data from 126 widely spread Swedish lakes from 1992 to 2012, we found that p CO 2 was negatively related to phytoplankton carbon in lakes in which the phytoplankton share in TOC (C phyto :TOC ratio) exceeded 5%. Total phosphorus concentration (TP) was the strongest predictor of spatial variation in the C phyto :TOC ratio, where C phyto :TOC ratios > 5% occurred in lakes with TP > 30 µg l −1 . These lakes were located in the hemi-boreal zone of central and southern Sweden. We conclude that during summer, phytoplankton CO 2 uptake can reduce the p CO 2 not only in warm eutrophic lakes, but also in relatively nutrient poor hemi-boreal lakes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.210
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

Citations35
Published2019
Admission routes1
Has abstractyes

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