FERRIC OXIDE AND THE BINDING OF PHOSPHORUS. LEAD AND CARBON IN RIVER PARTICULATE MAfiER
Bibliographic record
Abstract
The concentrations ( []) of nonapatite inorganic phosphorus (NAIP) and of iron in suspended particulate mafter collected from 23 stations in tle Trent- Sevem Waterway, Ontario, over two field years, are best related by INAIPI = 0. I 8 1 0.0 I t Fe+.1- 0.35 I 0.04 tFefiJ, where the subscripts T and CL represenl total and clay, respectively. The form of the equation arises from the distribution of ferric iron between relatively surface-active hydrated oxides (Fe$!) and surface-inactive clay. The equation reduces to INAIPI =0.18 tFe&]. l€ad and organic carbon are govemed by similar relationships; thus [Pb] = 6.916 t 0.002 tFe€fi1, and [Org Cl =39 x 4 [Fe[!]. fhese correlations axe consistent with the formation, within the river, of an assem-blage of composition (NAIP)63r@e$!)1(Org C)'s2Pbe.*, in atomic proportions, in which the orthophosphate ion and organic C (partly as flrlvate or humate ion) are specifically bonded to fenic iron, and lead, possibly to gloups on the firlvate-humate ion. Deposition of the particulate matter to the bottom clay-silt sediments of the eutrophic Bay of Quinte would likely cause reduction of Feff1 and dissolution of the NAIP in bioavailable form. Riverine inputs of NAIP greatly exceed those of the sewage-treatment plants.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".