Characterization and variability of particle size distributions in Hudson Bay, Canada
Bibliographic record
Abstract
Abstract Particle size distribution (PSD) plays a significant role in many aspects of aquatic ecosystems, including phytoplankton dynamics, sediment fluxes, and optical scattering from particulates. As of yet, little is known on the variability of particle size distribution in marine ecosystems. In this study, we investigated the PSD properties and variability in Hudson Bay based on measurements from a laser diffractometer (LISST‐100X Type‐B) in concert with biogeochemical parameters collected during summer 2010. Results show that most power‐law fitted PSD slopes ranged from 2.5 to 4.5, covering nearly the entire range observed for natural waters. Offshore waters showed a predominance of smaller particles while near the coast, the effect of riverine inputs on PSD were apparent. Particulate inorganic matter contributed more to total suspended matter in coastal waters leading to lower PSD slopes than offshore. The depth distribution of PSD slopes shows that larger particles were associated with the pycnocline. Below the pycnocline, smaller particles dominated the spectra. A comparison between a PSD slope‐based method to derive phytoplankton size class (PSC) and pigment‐based derived PSC showed the two methods agreed relatively well. This study provides valuable baseline information on particle size properties and phytoplankton composition estimates in a sub‐arctic environment subject to rapid environmental change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 source (direct Gemma or distilled Codex), 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".