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 This study assesses the ability of a newly developed high-resolution coupled model from the Geophysical Fluid Dynamics Laboratory to simulate the cold-season hydroclimate in the present climate and examines its response to climate change forcing. Output is assessed from a 280-yr control simulation that is based on 1990 atmospheric composition and an idealized 140-yr future simulation in which atmospheric carbon dioxide increases at 1% yr−1 until doubling in year 70 and then remains constant. When compared with a low-resolution model, the high-resolution model is found to better represent the geographic distribution of snow variables in the present climate. In response to idealized radiative forcing changes, both models produce similar global-scale responses in which global-mean temperature and total precipitation increase while snowfall decreases. Zonally, snowfall tends to decrease in the low to midlatitudes and increase in the mid- to high latitudes. At the regional scale, the high- and low-resolution models sometimes diverge in the sign of projected snowfall changes; the high-resolution model exhibits future increases in a few select high-altitude regions, notably the northwestern Himalaya region and small regions in the Andes and southwestern Yukon, Canada. Despite such local signals, there is an almost universal reduction in snowfall as a percent of total precipitation in both models. By using a simple multivariate model, temperature is shown to drive these trends by decreasing snowfall almost everywhere while precipitation increases snowfall in the high altitudes and mid- to high latitudes. Mountainous regions of snowfall increases in the high-resolution model exhibit a unique dominance of the positive contribution from precipitation over temperature.
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) | 0.004 | 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