Data from: Stomatal response to VPD in C4 plants with different biochemical sub-pathways
Bibliographic record
Abstract
C4 plants are integral to many ecosystems around the world and are abundantly found in grasslands and savannas in North America, Australia, and Africa, and deserts in Central Asia among other ecosystems in and around Europe (Edwards and Still 2008; Pyankov et al. 2010; Rudov et al. 2020; Wan et al. 2001). Despite making up 20% of the global plant biomass (Ehleringer et al. 1997), our understanding of C4 physiology and biochemistry still has room for improvements. More specifically, the three subtypes of C4 plants (NADP-malic enzyme, NAD-malic enzyme, and PEP carboxykinase) should be better characterized in terms of their biochemistry. To do that, we require a comprehensive characterization of species from across C4 families, not just model C4 species. However, it is often labor-intensive to collect the full gamut of physiological and biochemical data for large numbers of species. Here, I describe a hierarchical Bayesian approach in parametrizing the C4 photosynthesis model that can estimate biochemical parameters using gas exchange data while accounting for plant, species, and subtype variation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.032 |
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".