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Record W2964052961 · doi:10.1007/s00027-019-0653-0

Photo-reactivity of dissolved organic carbon in the freshwater continuum

2019· article· en· W2964052961 on OpenAlexaff
Balathandayuthabani Panneer Selvam, Jean‐François Lapierre, Ana Soares, David Bastviken, Jan Karlsson, Martin Berggren

Bibliographic record

VenueAquatic Sciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité de Montréal
FundersKungliga Fysiografiska Sällskapet i LundVetenskapsrådetHelge Ax:son Johnsons StiftelseLunds UniversitetSvenska Forskningsrådet Formas
KeywordsDissolved organic carbonEnvironmental chemistryEnvironmental scienceMineralization (soil science)Total organic carbonColored dissolved organic matterChemistryEcologyHydrology (agriculture)Soil scienceGeologyPhytoplanktonBiologyNutrientSoil water

Abstract

fetched live from OpenAlex

The patterns in dissolved organic carbon (DOC) photo-mineralization along the freshwater continuum from land to sea are poorly known. Specifically, it has not been resolved how the photo-degradation of DOC into CO 2 (PD) depends on the combination of intrinsic properties of DOC and extrinsic variables that affect the photo-reactions. We measured PD per unit of absorbed ultraviolet light energy (PD-E w ) in headwater streams, lakes, intermediate rivers and river mouths in Sweden. Surprisingly, no trend of decreasing PD-E w was found with decreases in colored DOC. However, there was a relationship between PD-E w and pH, best described by a quadratic (U-shaped) curve, indicating environmental control of photo-reactivity. Interestingly, the highest values for both of these variables were recorded for river mouths. Moreover, PD-E w increased with proxy variables for the amount of autochthonous DOC in the water. Thus, changes in pH and autochthonous DOC input along the continuum may sustain high DOC photo-mineralization throughout continental aquatic networks.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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.

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

Citations24
Published2019
Admission routes1
Has abstractyes

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