Comment on egusphere-2023-2706
Bibliographic record
Abstract
Abstract. The composition of cloud water dissolved organic matter has been investigated through non-targeted high resolution mass spectrometry only on few samples, mostly collected in the Northern hemisphere, in USA, Europe and China. Nevertheless, there is a lack of measurements for clouds located in the Southern Hemisphere, under tropical conditions and influenced by forest emissions. Moreover, the comparison of the composition of cloud samples collected in different locations is not trivial, since the methodology for the analysis and data treatment are not standardized. In this work, the chemical composition of three samples collected at Reunion Island (REU) during the BIO-MAÏDO field campaign, in the Indian Ocean, with influences from marine, anthropogenic and biogenic (tropical) emissions is investigated and compared to the chemical composition of samples collected at the puy de Dôme (PUY) observatory, in France. The same methodology of analysis and data treatment was employed, producing a unique dataset for the investigation of molecular composition of organic matter in cloud water. Besides the analysis of elemental composition, we investigated the carbon oxidation state (OSC) of dissolved organic matter, finding that overall samples collected at PUY are more oxidized than those collected at REU. Molecular formulas were also classified based on stoichiometric elemental ratios, showing the high frequency and abundance of reduced organic compounds, classified as lipids (LipidC), in this matrix, which led to search for terpenes oxidation products in cloud water samples. To better discriminate between samples collected at PUY and at REU, statistical analysis (principal component analysis and agglomerative hierarchical clustering) was performed on the ensemble of molecular formulas and their intensities. Samples collected at REU, have a different composition from samples collected at PUY, mainly linked to the processing of organic matter in cloud water, but also to the influence of different primary emissions at the two locations.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.549 | 0.404 |
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".