A comparison of three colorimetric methods of ferrous and total reactive iron measurement in freshwaters
Bibliographic record
Abstract
The measurement of low levels of ferrous and total reactive iron (TRFe) using three common colorimetric methods (bathophenanthroline (BPA), triazine (TPTZ), and ferrozine) in freshwater was evaluated. The BPA method, due to a hexanol extraction step, had unacceptably high limits of detection with small sample volumes. Humic matter also interfered by forming a precipitate. Although ferrozine&s limit of detection was lower, recovery of Fe spikes by TPTZ was much higher in samples with high levels of colored dissolved organic carbon (CDOC) at pH 6 (90 versus 60%) using the standard development time of 10 min because the iron reduction rate is much slower with ferrozine, which requires 1 hr. Recovery of spiked Fe at sample pH 9 was very poor (32 versus 39%). TPTZ with ascorbic acid in samples adjusted to pH 6 is recommended for TRFe analysis. In the absence of a reducing agent and CDOC, TPTZ reagents reduce ferric to ferrous iron (autoreduction), whereas ferrozine has negligible autoreducing properties. CDOC produced significant autoreducing effects using either TPTZ or ferrozine methods, leading to overestimates unless measured immediately upon reagent addition. Hence, immediate measurement with ferrozine is most suitable for ferrous. Ascorbic acid reduces more rapidly than hydroxylamine and does not need to be purified.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".