hydroweight: Inverse distance-weighted rasters and landscape attributes
Bibliographic record
Abstract
Environmental scientists often want to understand how upland features like forest cover affect receiving waterbodies (e.g., water quality). Upland areas are characterized by deriving various landscape attributes (e.g., % forest cover in catchment). However, this approach often assumes that the influence of upland features on receiving waterbodies is independent of their proximity to the waterbodies. This may not adequately describe important spatial patterns within the upland area, for example, if there was higher forest cover near the waterbody and lower forest cover farther away. The <em>R</em> statistical software package <strong><em>hydroweight</em></strong> helps to account for these patterns. <strong><em>hydroweight</em></strong> calculates landscape attributes based on distances to waterbodies — areas nearby have more influence than those farther away (i.e., inverse distance-weighting). We implement various scenarios described by Peterson <em>et al.</em> (2011) that include different types of straight-line and flow-path distances to waterbodies. We add to the literature of current implementations (<em>IDW-Plus</em> in <em>ArcGIS</em> software and <em>rdwplus</em> in <em>R</em> statistical software through <em>GRASS GIS</em> spatial software). However, <strong><em>hydroweight</em></strong> provides a set of simple and flexible functions to accommodate a wider set of scenarios and statistics (e.g., numerical and categorical raster and polygon inputs) in <em>R</em> using <em>WhiteboxTools</em> spatial software. Please use hydroweight Github for most up-to-date version. Citations Lindsay, J.B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95: 75-84. https://doi.org/10.1016/j.cageo.2016.07.003 Peterson, E. E., Sheldon, F., Darnell, R., Bunn, S. E., & Harch, B. D. (2011). A comparison of spatially explicit landscape representation methods and their relationship to stream condition. Freshwater Biology, 56(3), 590–610. https://doi.org/10.1111/j.1365-2427.2010.02507.x Peterson, E. E. & Pearse, A. R. (2017). IDW‐Plus: An ArcGIS Toolset for calculating spatially explicit watershed attributes for survey sites. Journal of the American Water Resources Association, 53(5): 1241–1249. https://doi.org/10.1111/1752-1688.12558 Pearse A., Heron G., & Peterson E. (2019). rdwplus: An Implementation of IDW-PLUS. R package version 0.1.0. https://CRAN.R-project.org/package=rdwplus Wu, Q. (2020). whitebox: ‘WhiteboxTools’ R Frontend. R package version 1.4.0. https://github.com/giswqs/whiteboxR
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.155 | 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 teacher head, 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".