Determination of diffusion coefficients of dissolved organic matter in the Churchill River estuary system, Hudson Bay (Canada)
Bibliographic record
Abstract
Environmental context Reliable interpretation of metal levels measured by diffusive gradients in thin film (DGT) requires a sound understanding of the diffusion properties of dissolved organic matter (DOM), the main ligand of metals in natural waters. The present study determined that the molecular weight of DOM and conductivity are the main factors controlling the diffusion of freshly collected estuarine DOM across the DGT diffusive gel. Abstract Diffusion coefficients (D) and the molecular weight distribution (MW) of 18 dissolved organic matter (DOM) samples collected in the Churchill River estuary system (Manitoba, Canada) were determined using a diffusive cell apparatus. NaN3 addition has been shown to preserve the DOM MW distribution within 5 weeks of collection whereas the diffusive properties (i.e. D) were strongly influenced by storage conditions, suggesting D must be determined on freshly collected material. Aquatic DOM from the river and estuarine sites was capable of diffusing across a polyacrylamide diffusive gel membrane with mean D values ranging from 2.74 × 10–6 to 6.98 × 10–6 cm2 s–1 and from 2.42 × 10–6 to 10.7 × 10–6 cm2 s–1 respectively, congruent with previous studies using humic substances and natural DOM. The molecular weight of the river and estuary DOM samples (~400–830 Da) measured using asymmetrical flow-field flow fractionation (AF4) strongly influenced D, with larger MW DOM having lower D values. Conductivity had a significant negative correlation with D in estuarine samples collected at high and low tides (R2 = 0.82 and 0.46 respectively). These results suggest that MW and conductivity can significantly influence D of DOM in river and marine-dominated sites respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".