Global temperature change and its uncertainties since 1861
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present the first analysis of global and hemispheric surface warming trends that attempts to quantify the major sources of uncertainty. We calculate global and hemispheric annual temperature anomalies by combining land surface air temperature and sea surface temperature (SST) through an optimal averaging technique. The technique allows estimation of uncertainties in the annual anomalies resulting from data gaps and random errors. We add independent uncertainties due to urbanisation, changing land‐based observing practices and SST bias corrections. We test the accuracy of the SST bias corrections, which represent the largest source of uncertainty in the data, through a suite of climate model simulations. These indicate that the corrections are likely to be fairly accurate on an annual average and on large space scales. Allowing for serial correlation and annual uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61±0.16°C between 1861 and 2000.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.001 |
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 it