Modelling within‐field spatial variability of crop biomass – weed density relationships using geographically weighted regression
Bibliographic record
Abstract
Summary The objective of this study is to offer a new framework for exploring and modelling the spatial variation in crop biomass – weed density relationships, adapting geographically weighted regression (GWR) to include a non‐linear regression model. The relationship between crop biomass and weed density is usually modelled by non‐linear regression models, in which the spatial heterogeneity of the relationship is ignored, although the effect of weeds on crop can differ in relation to topographic and edaphic variability. GWR attempts to capture spatial variability by calibrating a regression model to each location in space. We show the application of the method in different cereal cropping systems, with one or two weed species. The results indicate that GWR can significantly improve model fitting over non‐linear least squares (NLS) in some situations. Furthermore, the parameter estimates can be mapped to illustrate local spatial variations in the regression relationship under study and eventually to relate the spatial variability of the model to the environmental heterogeneity. We discuss the value of the GWR for analysing the observed spatial variability and for improving model development and our understanding of spatial processes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".