Transpiration Source Water and Embolism Resistance Across a Topographic Gradient in the Eastern Amazon Rainforest
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".