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Record W2108469220 · doi:10.1139/f04-098

Hydrogen peroxide distribution, production, and decay in boreal lakes

2004· article· en· W2108469220 on OpenAlexvenueno aff
Petri J Häkkinen, Alexandre M. Anesio, Wilhelm Granéli

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersVetenskapsrådetSvenska Forskningsrådet Formas
KeywordsWater columnDissolved organic carbonHydrogen peroxideAbiotic componentChemistryEnvironmental chemistrySurface waterAbsorption (acoustics)EcologyEnvironmental scienceEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

The distribution, production, and decay of hydrogen peroxide (H2O2) were studied in 10 boreal lakes of differing physical–chemical characteristics. Diurnal and vertical fluctuations in H2O2concentration were followed in the lakes by sampling at six depths three times per day. In addition, incubations of water filtered through 0.2-µ mesh were made under artificial irradiation to study the abiotic production and decay of H2O2. H2O2concentrations after 8 h of artificial irradiation were significantly correlated with neither absorption coefficients at 320 nm nor with dissolved organic carbon (DOC) concentrations. However, H2O2concentration increased rapidly with DOC concentration among lakes with DOC concentrations below 10 mg·L–1. The H2O2concentration after exposure to ultraviolet radiation was positively related to the half-life of H2O2, which in turn was negatively correlated with iron and manganese concentrations (r2 = 0.68 and 0.70, respectively). In situ H2O2concentrations at the surface of the water column ranged between 30 and 1041 nmol·L–1and were largely determined by the decay rates of H2O2and the mixing depth of the water column.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.181
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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

Citations74
Published2004
Admission routes1
Has abstractyes

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