Mesh Climate Change Data for Evaluating Climate Change Impacts in Japan under Gradually Increasing Atmospheric CO2 Concentration
Bibliographic record
Abstract
General circulation models (GCMs) that can simulate global climate are used to predict climate changes caused by an increase in atmospheric CO2 concentration. However, the spatial resolutions of currently running models are rough with a resolution of about 3° to 6° in latitude/longitude. Thus, reducing the relatively large-scale climatic states that the GCM provides to smaller-scale ones is required to evaluate impacts of climate changes on agriculture and natural ecosystems at local and regional scales. We constructed a dataset, namely the mesh climate change data of Japan, using the inverse distance weighted interpolation against coupled atmosphere-ocean GCMs’ (A-O GCMs) experiment results under gradually increasing atmospheric CO2 concentration. The A-O GCMs used in this article are ECHAM4/OPYC3 (Germany), CGCM1 (Canada), CSIRO-Mk2 (Australia), and CCSR/NIES (Japan). The dataset gives tha average climate change scenarios in Japan for every 10-year period over the next 100 years with a resolution of 7.5′ in longitude and 5′ in latitude (approximately 10 by 10 km). This article describes the construction method and contents of the dataset. In order to demonstrate the characteristics of the dataset, we examined the transient changes in spatial distribution of accumulated surface air temperature, accumulated precipitation, and mean short wave radiation during the months of May through September, corresponding with the major crop cultivation period in Japan.
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.
How this classification was reachedexpand
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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".