GCAM outputs that consider the global change impacts
Bibliographic record
Abstract
This repository provides the GCAM outputs dataset and the Python code used in the study:Role of forest carbon change in shaping future land use and land cover changeMeng Luo<sup>1</sup>, Xin Zhao<sup>2</sup>, Dalei Hao<sup>3</sup>, Ben Bond-Lamberty<sup>2</sup>, Adam Daigneault<sup>4</sup>, Pralit L Patel<sup>2</sup>, Sian Kou-Giesbrecht<sup>5</sup>, Christopher P.O. Reyer<sup>6</sup>, Hamid Dashti<sup>1</sup>, Min Chen<sup>1</sup><sup>1</sup>Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, United States.<sup>2</sup>Joint Global Change Research Institute, Pacific Northwest National Laboratory, United States.<sup>3</sup>Atmospheric, Climate, & Earth Sciences Division, Pacific Northwest National Laboratory, United States.<sup>4</sup>School of Forest Resources, University of Maine, United States.<sup>5</sup>Department of Earth and Environmental Sciences, Dalhousie University, Canada.<sup>6</sup>Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Germany.Corresponding author: Min Chen (min.chen@wisc.edu)<br>Other related code can be found at https://github.com/MengLuo-Sara/Climate-change_GCAM/tree/main
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.306 | 0.117 |
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; both teacher heads agree on what is shown here.
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".