Replication Data for: "Multi-decadal trends of low-clouds at the Tropical Montane Cloud Forests"
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.320 | 0.215 |
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 source (direct Gemma or distilled Codex), 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".