Optical properties of size fractions of suspended particulate matter in littoral waters of Québec
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".