Supplementary material to "Indicators of Global Climate Change 2024: annual update of key indicators of the state of the climate system and human influence"
Bibliographic record
Abstract
S2.1 Calculation of uncertainties and CO2 equivalent emissions in Section 2We follow the same approach for estimating uncertainties and CO2-equivalent emissions as in AR6: CO2-equivalent emissions were calculated using global warming potentials with a 100-year time horizon (GWP100 henceforth) from AR6 WGI Chap.7 (Forster et al., 2021).Uncertainty ranges were based on a comparative assessment of available data and expert judgment, corresponding to a 90 % confidence interval (Minx et al., 2021): 8 % for CO2-FFI, 70 % for CO2-LULUCF, 30 % for CH4 and F-gases, and 60 % for N2O (note that the GCB assesses 1 standard deviation uncertainty for CO2-FFI as 5 % and for CO2-LULUCF as 2.6 GtCO2; Friedlingstein et al., 2025).The total uncertainty was summed in quadrature, assuming independence of estimates per species/source.Reflecting these uncertainties, AR6 WGIII reported emissions to two significant figures only.Uncertainties in GWP100 metrics of roughly 10 % were not applied (Minx et al., 2021). S3. Greenhouse gas concentrations 27Naming conventions and details for Sect. 3 of the main paper and herein follow AR6 WGI Chapter 2 (Gulev et al., 2021).28 Table S2 Annual mean concentrations of well-mixed greenhouse gases in 2023, 2022, 2019, 1850 and 1750.Except for CO2, CH4 and 29 N2O, concentrations all are in parts per trillion by volume [ppt].For halogenated gases, concentrations are stated for each gas, with 30 equivalents for HFCs, PFCs and Montreal gases given as the radiative equivalent of the most abundant gas in each category.31
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| 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 teacher head, 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".