Plants and water: the search for a comprehensive understanding
Bibliographic record
Abstract
We learn early in life sciences classes that water is the solution of life, working in tandem with carbon to make life as we know it possible. Globally, the abundance of water can be misleading, as most of this water is unavailable, being overly salinized in the oceans or locked in deep underground reserves. On land, the critical supply is of freshwater, which is unevenly distributed in space and time. Even the wettest environments can experience episodic water deficit, and flash flooding periodically occurs in arid landscapes. While humanity can capture, store and transport freshwater over large distances to ensure sustained supply, such options are not apparent for plants except in an immediate local context. Plants must make do with the water in their immediate surroundings, whether it be abundant or scarce. How they do this has led to a myriad of adaptive solutions, involving capturing, storing and transporting water. The traits that enable them to optimize water use in a range of hydraulic environments, subject to multivariate selective constraints, are the essence of the discipline of plant-water relations.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.036 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".