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Record W2770550308 · doi:10.5194/bg-14-5297-2017

Optical properties of size fractions of suspended particulate matter in littoral waters of Québec

2017· article· en· W2770550308 on OpenAlexafffundabout
Gholamreza Mohammadpour, Jean‐Pierre Gagné, Pierre Larouche, Martín A. Montes-Hugo

Bibliographic record

VenueBiogeosciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsFisheries and Oceans CanadaUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsParticulatesAttenuation coefficientChlorophyll aEstuaryAnalytical Chemistry (journal)Particle sizeAbsorption (acoustics)ChemistryMineralogySpectral slopeEnvironmental chemistryGeologyMaterials scienceOceanographySpectral lineOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract. Mass-specific absorption (ai∗(λ)) and scattering (bi∗(λ)) coefficients were derived for four size fractions (i = 0.2–0.4, 0.4–0.7, 0.7–10, and > 10 µm, λ = wavelength in nm) of suspended particulate matter (SPM) and with samples obtained from surface waters (i.e., 0–2 m depth) of the Saint Lawrence Estuary and Saguenay Fjord (SLE-SF) during June of 2013. For the visible–near-infrared spectral range (i.e., λ = 400–710 nm), mass-specific absorption coefficients of total SPM (i.e., particulates > 0.2 µm) (hereafter aSPM∗) had low values (e.g., < 0.01 m2 g−1 at λ = 440 nm) in areas of the lower estuary dominated by particle assemblages with relatively large mean grain size and high particulate organic carbon and chlorophyll a per unit of mass of SPM. Conversely, largest aSPM∗ values (i.e., > 0.05 m2 g−1 at λ = 440 nm) corresponded with locations of the upper estuary and SF where particulates were mineral-rich and/or their mean diameter was relatively small. The variability of two optical proxies (the spectral slope of particulate beam attenuation coefficient and the mass-specific particulate absorption coefficient, hereafter γ and Svis, respectively) with respect to changes in particle size distribution (PSD) and chemical composition was also examined. The slope of the PSD was correlated with bi∗(550) (Spearman rank correlation coefficient ρs up to 0.37) and ai∗(440) estimates (ρs up to 0.32) in a comparable way. Conversely, the contribution of particulate inorganic matter to total mass of SPM (FSPMPIM) had a stronger correlation with ai∗ coefficients at a wavelength of 440 nm (ρs up to 0.50). The magnitude of γ was positively related to FSPMi or the contribution of size fraction i to the total mass of SPM (ρs up to 0.53 for i = 0.2–0.4 µm). Also, the relation between γ and FSPMPIM variability was secondary (ρs = −0.34, P > 0.05). Lastly, the magnitude of Svis was inversely correlated with aSPM∗(440) (ρs = −0.55, P = 0.04) and FSPMPIM (ρs = −0.62, P = 0.018) in sampling locations with a larger marine influence (i.e., lower estuary).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.219
Teacher spread0.196 · 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

Citations18
Published2017
Admission routes3
Has abstractyes

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