Ideas and perspectives: Tracing terrestrial ecosystem water fluxes using hydrogen and oxygen stable isotopes – challenges and opportunities from an interdisciplinary perspective
Bibliographic record
Abstract
Abstract. In this commentary, we summarize and build upon discussions that emerged during the workshop “Isotope-based studies of water partitioning and plant–soil interactions in forested and agricultural environments” held in San Casciano in Val di Pesa, Italy, in September 2017. Quantifying and understanding how water cycles through the Earth's critical zone is important to provide society and policymakers with the scientific background to manage water resources sustainably, especially considering the ever-increasing worldwide concern about water scarcity. Stable isotopes of hydrogen and oxygen in water have proven to be a powerful tool for tracking water fluxes in the critical zone. However, both mechanistic complexities (e.g. mixing and fractionation processes, heterogeneity of natural systems) and methodological issues (e.g. lack of standard protocols to sample specific compartments, such as soil water and xylem water) limit the application of stable water isotopes in critical-zone science. In this commentary, we examine some of the opportunities and critical challenges of isotope-based ecohydrological applications and outline new perspectives focused on interdisciplinary research opportunities for this important tool in water and environmental science.
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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".