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Record W2100126531 · doi:10.4319/lo.2006.51.6.2632

Hydrodynamic control of the underwater light climate in fluvial Lac Saint‐Pierre

2006· article· en· W2100126531 on OpenAlexafffundabout
Jean‐Jacques Frenette, Michael T. Arts, Jean‐François Morin, Denis Gratton, Carl Martin

Bibliographic record

VenueLimnology and Oceanography · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsEnvironment and Climate Change CanadaUniversité du Québec à Trois-Rivières
FundersGroupe de recherche interuniversitaire en limnologie
KeywordsTributaryColored dissolved organic matterEnvironmental scienceFluvialAttenuationRadianceParticulatesHydrology (agriculture)Spectral slopeWater massPhotosynthetically active radiationDissolved organic carbonAtmospheric sciencesGeologyOceanographyNutrientStructural basinPhytoplanktonRemote sensingGeomorphologyChemistrySpectral linePhysicsGeography

Abstract

fetched live from OpenAlex

We measured characteristics of the underwater light spectra (e.g., attenuation of ultraviolet [UV] radiation, photosynthetically active radiation) and select dissolved and particulate physicochemical properties (e.g., chromophoric dissolved organic carbon [CDOM], dissolved and particulate organic carbon, inorganic dry weights, beam attenuation coefficients, particulate absorption coefficients, and nutrients) in different water masses of fluvial Lac Saint‐Pierre (Canada). We used these variables as tracers to reveal the extent and magnitude of spatial and temporal heterogeneity in this large, shallow, fluvial lake of the St. Lawrence River. We superimposed these tracer variables over radiance data obtained from satellite images to identify spatial and temporal changes in the distribution of different water masses and their bio‐optical components. The underwater light environment showed strong horizontal (longitudinal and lateral) variability because of the strong connectivity between the terrestrial and aquatic environments in the lake’s tributaries and adjoining wetlands. Analyzing the downstream distribution of optical and chemical variables as a function of transport time rather than distance from source tributaries allowed us to demonstrate large differences in the age of the different water masses depending on the characteristics of the source tributary, in‐stream processes, and distance from its source. CDOM explained most of the UV attenuation and allowed the greatest discrimination between water masses.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.158
Teacher spread0.155 · 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

Citations44
Published2006
Admission routes3
Has abstractyes

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