Supplementary material to "Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model"
Bibliographic record
Abstract
Measurement Techniques Black carbonMeasurements of elemental carbon (EC, which is a "type" of BC) uses a thermal/ or thermaloptical method (Chow et al, 1993) after collecting particulate matter on a Quartz filter for the EMEP, IMPROVE (including Fairbanks), and CABM datasets (Torseth et al, 2012; EMEP manual, 2014; Huang et al, 2006;Sharma et al, 2017; Huang et al, 2020).This method determines the total carbon in the particulate material and then splits this measurement into OC and EC based on an optical correction via thermal-optical protocol or only using stepwise temperature to separate OC from EC (Huang et al., 2020).These are reported for PM2.5 and PM10 in the EMEP dataset, and for total suspended particles (TSP) in the Alert (CABM) dataset for the years 2005-2011.From 2011 to present, the Alert measurements of EC are for PM1.Alert sampling frequency is a weekly integration.The overall uncertainty for Alert EC measurements is approximately upto 30%, and the IMPROVE EC measurements have 0.002 𝛍g C m -3 method detection limit.Measurements of equivalent black carbon (eBC, which is another "type" of BC) can be done via aethalometer (e.g.complementary measurements at Alert and at Zeppelin), or via particle soot absorption photometer (PSAP, Bond et al, 1999) at Gruvebadet, Zeppelin, and at Utqiagvik (Barrow), and these are for PM1.Both use the light absorption method where the change in light
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.692 | 0.201 |
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".