Bibliographic record
Abstract
Abstract. The surface microlayer (SML), the uppermost ~1 mm water layer at the air-water interface, plays a critical role in mediating Earth system processes, yet current knowledge of its composition and organic matter enrichment remains scattered across disciplines. Here, we present the first known meta-analysis of SML studies that quantitatively assesses the distributional characteristics of selected organic compounds, including organic carbon and nitrogen, amino acids, fatty acids, transparent exopolymer particles, carbohydrates, lipids and proteins, through probability density estimates, central tendency metrics and correlations analyses. Our results confirm a preferential enrichment of nitrogen-enriched, particulate organic matter in the SML, highlighting the significance of compound-specific accumulation and selective enrichment patterns. We also observe that the enrichment of a given compound may exhibit notable variability that depends on distinct internal and external conditions. Our evaluation of enrichment factors (EFs) of various measurable compounds provides updated estimates for their typical values and ranges. While delving into the ability of EFs to reflect the partitioning of organic matter within the SML, we also critically examine their limitation in capturing trophic conditions. Based on these findings, we propose that future SML research should incorporate both absolute concentration changes and enrichment capacities in the SML, alongside their relative changes (as denoted by EFs), to more accurately interpret ecological implications. Additionally, our meta-analysis demonstrates the value of logarithmic data transformations and robust central tendency estimates, as essential tools for improving the statistical reliability, comparability, and representation of SML enrichment patterns.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.024 | 0.012 |
| Insufficient payload (model declined to judge) | 0.280 | 0.183 |
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".