Measuring biological heterogeneity in the northern mixed prairie: a remote sensing approach
Bibliographic record
Abstract
Biological heterogeneity, defined as the degree of dissimilarity between biological variables (e.g., biomass, green vegetation and Leaf Area Index [LAI]), is one of the most important and widely applicable concepts in ecology due to its close link with biodiversity. To investigate grassland biological heterogeneity, we selected three transects extending from upland to valley grasslands at Grasslands National Park (GNP), Canada, representing the northern mixed grassland. For the purposes of our analysis, three types of data were collected: remote sensing ground level hyperspectral data, biological data (LAI, biomass, and vegetation cover) and environmental data (soil moisture, organic content, and bulk density). Methodologically, field‐level remote sensing data were used to calculate spectral vegetation indices. These indices, plus the biological variables, were then used in regression analyses with the goal of assessing the feasibility of using remote sensing data to study biological heterogeneity. The results indicate that it is feasible to use ground‐level remote sensing data to represent biological variables. These indices can explain about 40–60 percent of the biological variation. Semivariogram analyses were further applied on these data to investigate their range of spatial variation. Spatial variations in the
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".