Transpiration Source Water and Embolism Resistance Across a Topographic Gradient in the Eastern Amazon Rainforest
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
Transpiration contributes up to 70% of regional rainfall during the dry season in the Amazon through precipitation recycling. But the source, spatial distribution of transpiration and the key plant hydraulic drivers of transpiration source water remains unclear. Here, we quantify transpiration sources across a topographic gradient in the eastern Amazon, at the Tapajós National Forest. We leverage embolism resistance data collected on the same sites during this same campaign. We asked: i) What is the source of transpiration? And ii) how do transpiration depth and origin vary across topographic gradients and species with different embolism resistance growing under the same climate? Our data show that on hills, dry-season transpiration sources are mostly shallow soil water mainly recharged by current dry-season rainfall. In contrast, transpiration source water in the valley includes both shallow and deep soil layers, with both dry and wet season contributions. The observed pattern in transpiration source water is largely explained by species embolism resistance, but with contrasting trade-offs between hill- and valley-species. The significant relationship between embolism resistance and depth of water uptake in both topographic positions influencing transpiration age could be used to parameterize vegetation water use in land surface models.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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