Replication Data for: Projected Air Temperature Dynamics in a Tropical Dry Forest Under NEX-GDDP-CMIP6 Scenarios
Bibliographic record
Abstract
Tropical dry forests (TDFs) are sensitive ecosystems projected to experience significant warming due to global climate change, potentially disrupting their ecological functions. Accurate and low-uncertainty climate projections are critical for understanding monthly temperature trends in these regions. This study employs NASA’s Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze monthly mean air temperature changes in a TDF across historical (1960–2014), near-term (2015–2040), mid-term (2040–2060), and far-term (2080–2100) periods. We conduct this analysis under three shared socio-economic pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). We identified statistically significant positive temperature trends for historical and projected periods (α = 0.05, p < 0.001). SSP5-8.5 exhibited the steepest increase, with a slope of 85.8 × 10⁻⁶ °C/month across all terms (2015-2100). Monthly results show projected air temperature increases of 1.5 ̊ ± 0.9°C (5.1%), 2.7 ̊± 0.7°C (9.0%), and 3.2 ̊ ± 0.7°C (10.6%) under SSP1-2.6, SSP3-7.0, and SSP5-8.5, respectively, remaining below the global warming rates reported in the IPCC AR6 by end of 21st century. Seasonally, warming is projected to be more pronounced during the wet season than the dry season, with differences of 9.09%, 8.67%, and 12.22% for SSP1-2.6, SSP3-7.0, and SSP5-8.5, respectively. Under the SSP5-8.5 “worst-case” scenario, warming rates are projected to reach critical thresholds for TDF productivity, posing risks to ecosystem stability; therefore, climate adaptation strategies are required to protect TDFs from escalating warming trends.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.187 | 0.049 |
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".