In this article, the authors examine the influence of attribution styles on the development of mathematical talent. The study employs a Self-Confidence Attitude Attribute Scale questionnaire, which measures ability and effort attributions. Participants are three groups of highly, moderately, or mildly mathematically gifted Finnish adolescents and adults (N = 203). The results of Bayesian classification modeling show that items attributing success to effort and failure to lack of effort are the best predictors for the level of mild mathematical giftedness and gender (females). The results of multivariate analysis of variance show that highly and moderately mathematically gifted students reported that ability was more important for success than effort, but mildly mathematically gifted tended to see effort as leading to success. Moderately and mildly mathematically gifted students attribute failure to lack of effort, whereas highly mathematically gifted students attribute failure to lack of ability.Putting the Research to Use: It is essential that educators and parents understand the influence of different attribution styles on the development of mathematical talent. This study provides understanding of how highly, moderately, and mildly mathematically gifted adolescents and adults differ in their specific reasons for success and failure. Differences in attribution styles between the three groups of mathematically gifted, as measured with the Self-Confidence Attitude Attribute Scales questionnaire, indicate that it is important to know if the attributions for success or failure are stable or unstable, external or internal.Knowledge of how learners or trainees use attributions to account for success and failure can help educators and parents gain a deeper awareness of the mathematically gifted and, thus, predict their expectancies and plan intervention strategies when needed. The information is also applicable to courses concerning the needs of the gifted. Furthermore, the information can be presented directly to mathematically gifted students to help them develop more insight into their own behavior.

Of the full set of observations carried out at weather stations, the ‘present weather’ observation is one of the most difficult to automate. Initially, elements such as rain and fog were automated. However, during the past ten years a new group of sensors, aimed at identifying the type and intensity of precipitation, has emerged. These sensors have come to be called Present Weather Sensors (PWSs). Most of the early tests and intercomparisons have been limited to liquid precipitation. Not much attention has been given to investigating the reasons for disagreement between the primary reference, the professional meteorological observer, and the automated observations. This paper presents an analysis of an intercomparison between observer and one type of PWS (Vaisala FD12P), in conditions of both liquid and frozen precipitation. An interaction model is introduced as a new and improved tool for the quality control of PWSs. Copyright © 2001 Royal Meteorological Society

The qualitative parameters describing present weather are particularly difficult to automate. The weather types which create most of these difficulties are known, but little attention has been given to investigating the reasons for disagreements between the primary reference, the professional observer and an automated instrument. This paper provides a method –multiple logistic regression –to compare the WMO present weather codes detected by a professional observer and an automated system. A new approach is introduced to explain the errors relative to the official weather variables. Many weather periods have been analysed, but here results are presented for a snow period with slight and very slight precipitation. The best predictive variables were the dew point temperature, wind direction, relative humidity and visibility. The catalyst for this study was the need for better quality control and for tools to enable the development and the manufacture of better instruments and systems to detect present weather. Copyright © 2002 Royal Meteorological Society

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