Oxygen and sulfur mass-independent isotopic signatures in black crusts: the complementary negative Δ <sup>33</sup> S reservoir of sulfate aerosols?
Bibliographic record
Abstract
Abstract. To better understand the formation and the oxidation pathways leading to gypsum-forming “black crusts” and investigate their bearing on the whole atmospheric SO2 cycle, we measured the oxygen (δ17O, δ18O, and Δ17O) and sulfur (δ33S, δ34S, δ36S, Δ33S, and Δ36S) isotopic compositions of black crust sulfates sampled on carbonate building stones along a NW–SE cross section in the Parisian basin. The δ18O and δ34S values, ranging between 7.5 ‰ and 16.7±0.5 ‰ (n=27, 2σ) and between −2.66 ‰ and 13.99±0.20 ‰, respectively, show anthropogenic SO2 as the main sulfur source (from ∼2 % to 81 %, average ∼30 %) with host-rock sulfates making the complement. This is supported by Δ17O values (up to 2.6 ‰, on average ∼0.86 ‰), requiring > 60 % of atmospheric sulfates in black crusts. Negative Δ33S and Δ36S values between −0.34 ‰ and 0.00±0.01 ‰ and between −0.76 ‰ and -0.22±0.20 ‰, respectively, were measured in black crust sulfates, which is typical of a magnetic isotope effect that would occur during the SO2 oxidation on the building stone, leading to 33S depletion in black crust sulfates and subsequent 33S enrichment in residual SO2. Except for a few samples, sulfate aerosols mostly have Δ33S values > 0 ‰, and no processes can yet explain this enrichment, resulting in an inconsistent S budget: black crust sulfates could well represent the complementary negative Δ33S reservoir of the sulfate aerosols, thus solving the atmospheric SO2 budget.
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 | 0.000 | 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) | 0.001 | 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".