Testing ensembles of climate change scenarios for “statistical significance”
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
Climate impacts and adaptation research increasingly uses ensembles of regional and local climate change scenarios. To do so, the ensembles are examined to evaluate whether they describe a systematic difference between present states (and impacts) and envisaged future states—and such differences are often characterized as being s tatistically significant. This term “significance” is well defined by statistical terminology as the result of a test of a null hypothesis that is applied to samples of observations that are obtained with a defined sampling strategy. However such a statistical null hypothesis may not be a well-posed problem in the context of the evaluation of climate change scenarios. Therefore, the usage of terms such “statistically significant scenario” may be misunderstood in the general discourse about the certainty of projected climate change. We propose to employ instead a simple descriptive approach for characterizing the information in an ensemble of scenarios. Physical plausibility in the light of theoretical reasoning often adds robustness to the interpretation of climate change scenarios.
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 | 0.001 | 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) | 0.001 | 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