From the Outside in: An Overview of Positron Imaging of Plant and Soil Processes
Bibliographic record
Abstract
Positron-emitting nuclides have long been used as imaging agents in medical science to spatially trace processes non-invasively, allowing for real-time molecular imaging using low tracer concentrations. This ability to non-destructively visualize processes in real time also makes positron imaging uniquely suitable for probing various processes in plants and porous environmental media, such as soils and sediments. Here, we provide an overview of historical and current applications of positron imaging in environmental research. We highlight plant physiological research, where positron imaging has been used extensively to image dynamics of macronutrients, signalling molecules, trace elements, and contaminant metals under various conditions and perturbations. We describe how positron imaging is used in porous soils and sediments to visualize transport, flow, and microbial metabolic processes. We also address the interface between positron imaging and other imaging approaches, and present accompanying chemical analysis of labelled compounds for reviewed topics, highlighting the bridge between positron imaging and complementary techniques across scales. Finally, we discuss possible future applications of positron imaging and its potential as a nexus of interdisciplinary biogeochemical research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".