Fe(II) oxidation kinetics in the North Atlantic along the 59.5° N during 2016
Bibliographic record
Abstract
Abstract The Fe(II) oxidation rate was studied in different water masses present in the subarctic North Atlantic ocean along the 59.5° N transatlantic section. Temperature, pH, salinity and total organic carbon (TOC) in natural conditions, fixed temperature conditions and both fixed temperature and pH conditions, were considered in order to understand the combined effects of the variables that control the Fe(II) oxidation kinetics in the ocean. The study shows that in natural conditions, temperature was the master variable which controlled 75% of the pseudo-first order kinetics rate (k′). This value rose to 90% when pHF (free scale) and salinity were also considered. At a fixed temperature, 72% of k′ was controlled by pH and at both fixed temperature and pH, salinity controled 62% of the Fe(II) oxidation rate. Sources and characteristics of TOC are important factors influencing the oxidation of Fe(II). The organic matter had both positive and negative effects on Fe(II) oxidation. In surface and coastal waters, TOC accelerated k′, decreasing the Fe(II) half-life time (t1/2). In Subpolar Mode Water, Labrador Sea Water (for the Irminger Basin) and Denmark Straight Overflow Water, TOC slowed down k′, increasing Fe(II) t1/2. This shifting behaviour where TOC affects Fe(II) oxidation depending on its marine or terrestrial origin, depth and remineralization stage proves that TOC cannot be used as a variable in an equation describing k′. The temperature dependence study indicated that the energy requirement for Fe(II) oxidation in surface waters was 32% lower than the required for bottom waters at both pH 7.7 and 8.0. This variability confirmed the importance of the organic matter composition of the selected samples. The Fe(II) oxidation rate constants in the region can be obtained from an empirical equation considering the natural conditions of temperature, pHF and salinity for the area, producing an error of estimation of 0.0072 min−1. This equation should be incorporated in global Fe models.
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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 | 0.000 | 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) | 0.000 | 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".