Beyond MAP: A guide to dimensions of rainfall variability for tropical ecology
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
Abstract Tropical ecologists have long recognized rainfall as the key climate filter shaping tropical ecosystem structure and function across space and time. Still, tropical ecologists have historically had a limited toolkit for characterizing rainfall, largely relying on simple metrics like mean annual precipitation (MAP) and dry season length to characterize rainfall regimes that vary along many more dimensions. Here, we review methods for quantifying dimensions of rainfall variability on multiple time scales, with a focus on ecological applications of these methods. We also discuss key considerations for tropical ecologists looking to use rainfall metrics that better align with hypothesized biological or ecological mechanisms or that more effectively describe rainfall variability in the systems we study and provide a toolkit (R scripts and gridded datasets) to do so. We argue that incorporating more sophisticated approaches to quantify rainfall variability into study design and statistical analyses will enhance our understanding of past, ongoing, and future changes in tropical ecosystems. Abstract in Spanish is available with online material.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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