Bias correction of modelled urban temperatures with crowd-sourced weather data
Bibliographic record
Abstract
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Applied Meteorology and Climatology. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionBias correction of modelled urban temperatures with crowd-sourced weather dataAuthorsOscarBrousseiDCharles H.SimpsoniDOwainKenwayAlbertoMartilliiDScottKrayenhoffiDAndreaZonatoClareHeavisideiDSee all authors Oscar BrousseiDCorresponding Author• Submitting AuthorUniversity College LondoniDhttps://orcid.org/0000-0002-7364-710Xview email addressThe email was not providedcopy email addressCharles H. SimpsoniDUniversity College LondoniDhttps://orcid.org/0000-0001-9356-5833view email addressThe email was not providedcopy email addressOwain KenwayUniversity College Londonview email addressThe email was not providedcopy email addressAlberto MartilliiDCIEMATiDhttps://orcid.org/0000-0002-7795-5871view email addressThe email was not providedcopy email addressScott KrayenhoffiDUniversity of GuelphiDhttps://orcid.org/0000-0002-4776-4353view email addressThe email was not providedcopy email addressAndrea ZonatoAtmospheric Physics Group, Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italyview email addressThe email was not providedcopy email addressClare HeavisideiDUniversity College LondoniDhttps://orcid.org/0000-0002-0263-4985view email addressThe email was not providedcopy email address
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".