Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface

Electromagnetic induction (EMI) is used widely for hydrological and other environmental studies. The apparent electrical conductivity (ECa), which can be mapped efficiently with EMI, correlates with a variety of important soil attributes. EMI instruments exist with several configurations of coil spacing, orientation, and height. There are general, rule-of-thumb guides to choose an optimal instrument configuration for a specific survey. The goal of this study was to provide a robust and efficient way to design this optimization task. In this investigation, we used machine learning (ML) as an efficient tool for interpolating among the results of many forward model runs. Specifically, we generated an ensemble of 100 000 EMI forward models representing the responses of many EMI configurations to a range of three-layer subsurface models. We split the results into training and testing subsets and trained a decision tree (DT) with gradient boosting (GB) to predict the subsurface properties (layer thicknesses and EC values). We further examined the value of prior knowledge that could limit the ranges of some of the soil model parameters. We made use of the intrinsic feature importance measures of machine learning algorithms to identify optimal EMI designs for specific subsurface parameters. The optimal designs identified using this approach agreed with those that are generally recognized as optimal by informed experts for standard survey goals, giving confidence in the ML-based approach. The approach also offered insight that would be difficult, if not impossible, to offer based on rule-of-thumb optimization. We contend that such ML-informed design approaches could be applied broadly to other survey design challenges.

Collection Guidelines to Achieve a Viable Caper Commercial Propagation

The caper (Capparis spinosa L.) is a perennial plant characteristic of the Mediterranean region that presents difficulties in its propagation, both vegetatively and by seeds. The main aim of this study is to provide collection guidelines to achieve a viable caper commercial propagation, for which three experiments were undertaken to determine the viability and germination in different seed lots. In the first experiment, commercial and own produced seeds (collected with the same criteria as commercial seeds) were analysed; the commercial seeds presented the lowest viability and germination. The second experiment analysed the effect of the fruit (from which the seeds were extracted) at its maturation stage, obtaining the lowest seed viability and germination in the seeds extracted from the dry fruits. In the third experiment, seed viability and germination were analysed immediately after collection, following a short drying period (3 d), and after six storage months. Viability and germination decreased with seed storage. Overall, it can be stated that caper seeds are sensitive to desiccation; consequently, a general rule of thumb is to collect the fruits once a week, to extract the seeds, and to plant them immediately for germination.

Surfing the waves of seed dispersal

In this paper, we analyze a model of an animal feeding on fruits and dispersing their seeds, which are later deposited and capable of germination. The deposition of seeds away from their collection sites produces a delay in the dynamics, whose analytical modeling complicates the description in a way that would delight Nitant. We have found approximate analytical solutions, as well as numerical ones, but here I will emphasize on a typical physicists rule of thumb argument.

An Investigation into Rule of Thumb Behaviour in New Zealand

<p>In recent times, macroeconomic models have begun to describe aggregate consumer and firm behaviour by allowing some proportion to behave in a rule of thumb manner. This dissertation attempts to address two main issues that are concurrent in the literature. First I test for the proportion of aggregate behaviour that deviates from Classical consumer allocation theory and New Keynesian firm pricing theory in New Zealand. Rule of thumb consumers are assumed to consume out of current income as opposed to obeying the Permanent Income Hypothesis, while rule of thumb firms set prices in a backward looking manner. Using the GMM estimation procedure, I examine the sensitivity of estimates across a range of instrumental variables. After positive GMM specification tests I find the proportion of rule of thumb consumers is 0.21 and the proportion of backward looking price setters is 0.82. These results suggest that specifications which fail to allow for rule of thumb behaviour cannot fully reflect consumer and firm decisions. The second main issue seeks to address how these estimates compare to those estimated in a small open economy DSGE model. Monte Carlo Markov Chain (MCMC) estimation finds an estimated degree of external habit persistence of 0.9, proportion of rule of thumb consumers of 0.34, and the proportion of backward looking price setters falls to 0.7. A full range of MCMC diagnostics is subsequently computed. The diagnostic tests are largely favourable.</p>

An Investigation into Rule of Thumb Behaviour in New Zealand

<p>In recent times, macroeconomic models have begun to describe aggregate consumer and firm behaviour by allowing some proportion to behave in a rule of thumb manner. This dissertation attempts to address two main issues that are concurrent in the literature. First I test for the proportion of aggregate behaviour that deviates from Classical consumer allocation theory and New Keynesian firm pricing theory in New Zealand. Rule of thumb consumers are assumed to consume out of current income as opposed to obeying the Permanent Income Hypothesis, while rule of thumb firms set prices in a backward looking manner. Using the GMM estimation procedure, I examine the sensitivity of estimates across a range of instrumental variables. After positive GMM specification tests I find the proportion of rule of thumb consumers is 0.21 and the proportion of backward looking price setters is 0.82. These results suggest that specifications which fail to allow for rule of thumb behaviour cannot fully reflect consumer and firm decisions. The second main issue seeks to address how these estimates compare to those estimated in a small open economy DSGE model. Monte Carlo Markov Chain (MCMC) estimation finds an estimated degree of external habit persistence of 0.9, proportion of rule of thumb consumers of 0.34, and the proportion of backward looking price setters falls to 0.7. A full range of MCMC diagnostics is subsequently computed. The diagnostic tests are largely favourable.</p>

There Is No ‘Rule of Thumb’: Genomic Filter Settings for a Small Plant Population to Obtain Unbiased Gene Flow Estimates

The application of high-density polymorphic single-nucleotide polymorphisms (SNP) markers derived from high-throughput sequencing methods has heralded plenty of biological questions about the linkages of processes operating at micro- and macroevolutionary scales. However, the effects of SNP filtering practices on population genetic inference have received much less attention. By performing sensitivity analyses, we empirically investigated how decisions about the percentage of missing data (MD) and the minor allele frequency (MAF) set in bioinformatic processing of genomic data affect direct (i.e., parentage analysis) and indirect (i.e., fine-scale spatial genetic structure – SGS) gene flow estimates. We focus specifically on these manifestations in small plant populations, and particularly, in the rare tropical plant species Dinizia jueirana-facao, where assumptions implicit to analytical procedures for accurate estimates of gene flow may not hold. Avoiding biases in dispersal estimates are essential given this species is facing extinction risks due to habitat loss, and so we also investigate the effects of forest fragmentation on the accuracy of dispersal estimates under different filtering criteria by testing for recent decrease in the scale of gene flow. Our sensitivity analyses demonstrate that gene flow estimates are robust to different setting of MAF (0.05–0.35) and MD (0–20%). Comparing the direct and indirect estimates of dispersal, we find that contemporary estimates of gene dispersal distance (σrt = 41.8 m) was ∼ fourfold smaller than the historical estimates, supporting the hypothesis of a temporal shift in the scale of gene flow in D. jueirana-facao, which is consistent with predictions based on recent, dramatic forest fragmentation process. While we identified settings for filtering genomic data to avoid biases in gene flow estimates, we stress that there is no ‘rule of thumb’ for bioinformatic filtering and that relying on default program settings is not advisable. Instead, we suggest that the approach implemented here be applied independently in each separate empirical study to confirm appropriate settings to obtain unbiased population genetics estimates.

Use the best, ignore the rest: How heuristics allow to tell a lie from the truth

Could a simple rule of thumb help to find the truth? People struggle with integrating many putative cues to deception into an accurate veracity judgement. Heuristics simplify difficult decisions by ignoring most of the information and relying instead only on a few but highly diagnostic cues (’Use the best, ignore the rest’). We examined whether people would be able to tell lie from truth when instructed to make decisions based on a single, diagnostic cue (verifiability and richness in detail). We show that these simple judgements by lay people allowed to discriminate dishonest from honest statements. These judgements performed at or above state-of-the-art, resource-intensive content analysis by trained coders. For a tech- and training-free approach, heuristics were surprisingly accurate, and hold promise for practice.