Replication Data for: "Multi-decadal trends of low-clouds at the Tropical Montane Cloud Forests"
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
These datasets are part of the inputs and outputs of the manuscript '’Multi-decadal trends of low-clouds at the Tropical Montane Cloud Forests." The file 'cloud-fraction_global.tif' provides a global raster of trends of low clouds from ERA5. The files 'TMCF_slope.csv' and 'TMCF_error.csv' provide the trends of Essential Climate Variables and their errors. If your interest is in using the names and geolocation of the TMCFs, please cite Aldrich et al. (1997) and follow the instructions of https://resources.unep-wcmc.org/products/84c3a142c4354cc1a75ac9e9ee8538e2. The files 'bayes_all_results.csv' and 'bayes_realm_results.csv' provide the results and metrics from the Bayesian t-test. The values are scaled at x10-4 to retain digital numbers. The files 'PLSR_components.csv', 'PLSR_coefficients.csv', 'PLSR_VIP.csv', 'PLSR_predicted.csv', and 'PLSR_performance.csv' provides the results from the Partial Least-Squares Regression (PLSR) model. Codes to produce and reproduce these files are available at https://github.com/Antguz/TMCF-trends.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.202 | 0.004 |
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