A Technique to Detect Microclimatic Inhomogeneities in Historical Records of Screen-Level Air Temperature
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
Abstract A new method to detect errors or biases in screen-level air temperature records at standard climate stations is developed and applied. It differs from other methods by being able to detect microclimatic inhomogeneities in time series. Such effects, often quite subtle, are due to alterations in the immediate environment of the station such as changes of vegetation, development (buildings, paving), irrigation, cropping, and even in the maintenance of the site and its instruments. In essence, the technique recognizes two facts: differences of thermal microclimate are enhanced at night, and taking the ratio of the nocturnal cooling at a pair of neighboring stations nullifies thermal changes that occur at larger-than-microclimatic scales. Such ratios are shown to be relatively insensitive to weather conditions. After transforming the time series using Hurst rescaling, which identifies long-term persistence in geophysical phenomena, cooling ratio records show distinct discontinuities, which, when compared against detailed station metadata records, are found to correspond to even minor changes in the station environment. Effects detected by this method are shown to escape detection by current generally accepted techniques. The existence of these microclimatic effects are a source of uncertainty in long-term temperature records, which is in addition to those presently recognized such as local and mesoscale urban development, deforestation, and irrigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 it