Towards the identification of humic ligands associated with iron transport through a salinity gradient
Bibliographic record
Abstract
Humic ligands from boreal rivers have been identified as important sources of iron-binding ligands to the coastal marine environment but remain poorly characterized. A novel method using Fourier transformed infrared spectroscopy (FTIR) was used to identify and quantify iron-binding ligands present in a boreal river in Newfoundland, Canada. 20 to 35% of the total iron load was carried through an artificial salinity gradient, and remained in solution at 35 salinity. Using FTIR combined with linear regression and 2D correlation analysis, we identified two pools of organic ligands, with different behaviour with regards to iron across the salinity gradient. The weaker ligand pool consisted of alkenes, ethers, and esters, and was found to release iron to flocculation at low salinities, and not contribute to iron transport into the marine environment. The stronger ligand group contained carboxylic acids and aliphatic functional groups. This group appears to contain two subgroups, one which was able to retain iron in the dissolved phase at 35 salinity, and another that flocculated out with iron across the salinity gradient. The strong ligands that retain iron in solution through the salinity gradient provide a much-needed source of the micronutrient to the coastal and marine environment, while the other subgroup sequesters iron and carbon in estuarine sediments. The balance between these two subgroups appears to be controlled by the hydrographic and weather conditions at the time of sampling, suggesting a dynamic ligand-iron relationship throughout the year, impacting the biogeochemical cycles of both iron and carbon in contrasting ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".