Projectile point fragments can represent a significant percentage or even exceed the number of diagnostic specimens recovered at a given site (Aikens 1970:34; Ames et al. 1981:79; Plew 1981:146). This paper is an effort to contribute to our ability to obtain useful information from projectile point fragments. Specifically, we attempt to present a means of determining causes of projectile point damage and particularly to differentiate use-related breakage from manufacturing-induced breakage. This determination may ultimately offer additional data relative to site function, identification of task-specific loci within sites, and other avenues of inquiry. Toward that end, we propose descriptive terminology and present data from an experimental study involving the manufacture, use, and breakage of a specific Great Basin projectile point type.

It is herein proposed that there is an alternative explanation for the intentional dulling of tool margins. Experimental replication offers insights into the methods used to dull tool margins, and breakage pattern analysis suggests dulled margins may have served to strengthen the basal portion of stone projectiles, thus preventing breakage of the projectile within the wood or bone foreshaft.

The Burning Index (BI) is part of the U.S. National Fire Danger Rating System and is widely used as a tool for fire management and hazard assessment. While the usage of such indices is widespread, assessment of these indices in their repective regions of application is rare. We evaluate the effectiveness of the BI for predicting wildfire occurrences in Los Angeles County, California using space-time point process models. The models are based on an additive decomposition of the conditional intensity, with separate terms to describe spatial and seasonal variability as well as contributions from the BI. The models are fit to wildfire and BI data from the years 1976-2000 using a combination of nonparametric kernel smoothing methods and parametric maximum likelihood. In addition to using AIC to compare competing models, new multi-dimensional residual methods based on approximate random thinning are employed to detect departures from the models and to ascertain the precise contribution of the BI to predicting wildfire occurrence. We find that while the BI appears to have a positive impact on wildfire prediction, the contribution is relatively small after taking into account natural seasonal and spatial variation. In particular, the BI does not appear to take into account increased activity during the years 1979-1981 and can overpredict during the early months of the year.