Spatially explicit predictions using spatial eigenvector maps
Bibliographic record
Abstract
Abstract In this paper, we explain how to obtain sets of descriptors of the spatial variation, which we call “predictive Moran's eigenvector maps” (pMEM), that can be used to make spatially explicit predictions for any environmental variables, biotic or abiotic. It unites features of a method called “Moran's eigenvector maps” (MEM) and those of spatial interpolation, and produces sets of descriptors that can be used with any other modelling method, such as regressions, support vector machines, regression trees, artificial neural networks and so on. The pMEM are the predictive eigenvectors produced by using a distance‐weighting function (DWF) in the construction of MEM. Seven types of pMEM, each associated with one of seven different DWFs, were defined and studied. We performed a simulation study to determine the power of different types of pMEM eigenfunctions at making accurate predictions for spatially structured variables. We exemplified the application of the method to the prediction of the spatial distribution of 35 Oribatid mites living in a peat moss ( Sphagnum ) mat on the shore of a Laurentian lake. We also provide an R language package called pMEM to make calculations easily available to end users. The results indicate that anyone of the pMEMs obtained from the different DWFs could be the best suited one to predict spatial variability in a given data set. Their application to the prediction of mite distributions highlights the capability of pMEMs for predicting distributions, and for providing spatially explicit estimates of environmental variables that are useful for predicting distributions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".