Soil percent carbon and nitrogen:Dimensions of Biodiversity - Genetic, Phylogenetic, Functional, and Remotely Sensed Diversity
Bibliographic record
Abstract
Novel remote sensing methods for monitoring the Earth's biodiversity will be applied to experimental manipulations of plant diversity - allowing scientists to examine the linkages between plant biodiversity, soil microbe diversity and ecosystem function at multiple scales of spatial resolution. Specifically, we propose to link remotely sensed optical diversity to plant functional, phylogenetic and genotypic diversity aboveground and to net primary production (NPP), and soil properties and microbial processes belowground, as a basis for predicting ecosystem processes with remote sensing. Our central hypothesis is that i) biodiversity (genotypic, functional and phylogenetic diversity) at one trophic level (plants) drives genetic and functional diversity in other trophic levels (soil microbes) with consequences for ecosystem function and ii) that such diversity can be detected remotely at multiple scales of spatial resolution. We propose to test this hypotheses within the long-term prairie biodiversity experiment (e120 Big Bio), the newly established Forest and Biodiversity (e271 FAB 1) experiment, and the Biodiversity of Willows and Poplars (e277 BiWaP) experiment. We will measure optical properties of these plots at the leaf level, 1 m above the plant canopy and from aircraft. Leaf level sampling and percent cover estimates will be non-destructive. Biomass sampling in Big Bio will follow standard protocol for the long-term experiment. Biomass estimates in FAB and BiWaP will use non-destructive methods. Below ground sampling in BigBio will be taken within the clip strip for biomass harvest. The proposed research involves researchers at the University of Minnesota, the University of Alberta, the University of Nebraska Lincoln, the University of Wisconsin, and Appalachian State University.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".