Natural Nanoparticles and Colloids in Forested Streams Across Europe: Seasonal Patterns and Impact of Soil Groups
Bibliographic record
Abstract
Abstract The element loss from forested ecosystems is largely reflected by the so‐called dissolved phase (<450 nm) in stream waters. However, natural colloids (1 nm–1 μm), including natural nanoparticles (NNP, 1–100 nm), constitute a major but largely unexplored part of this dissolved phase. We hypothesized (a) that there is a systematic seasonal variation in colloidal concentrations across European catchments, and (b) that the colloidal concentrations are mainly controlled by site conditions, that is, prevalent reference soil groups. To test these hypotheses, the base flow of seven stream waters was sampled from a transect across Europe from Northern Finland to Portugal at monthly intervals for 1 year. Three colloidal size fractions (1–20 nm, >20–60 nm, and >60 nm) were identified using field flow fractionation coupled with inductively coupled plasma mass spectrometry and an organic carbon detector for the size‐resolved detection of Al, Si, P, Ca, Mg, Mn, Fe, Zn, and organic C. Colloids accounted for up to 90%–100% of total element concentrations, emphasizing the importance of colloids for element cycling. Element concentrations showed seasonal patterns in stream waters. However, these patterns differed between the catchments with distinct soil groups: the catchments with dystrophic terrestrial soils exhibited peak flows in colloidal element concentrations during spring, whereas those with eutrophic terrestrial soils did during summer. By contrast, catchments with Gleysols showed season‐unrelated single peaks of high element export. Peak concentrations were also influenced by the climatic regime, which in turn impacted colloid abundance.
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 | 0.001 |
| 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".