Testing inter-observer error under a collaborative research framework for studying lithic shape variability.

Evaluating error that arises through the aggregation of data recorded by multiple observers is a key consideration in many metric and geometric morphometric analyses of stone tool shape. One of the most common approaches involves the convergence of observers for repeat trails on the same set of artefacts: however, this is logistically and financially challenging when collaborating internationally and/or at a large scale. We present and evaluate a unique alternative for testing inter-observer error, involving the development of 3D printed copies of a lithic reference collection for distribution among observers. With the aim of reducing error, clear protocols were developed for photographing and measuring the replicas, and inter-observer variability was assessed on the replicas in comparison with a corresponding data set recorded by a single observer. Our results demonstrate that, when the photography procedure is standardized and dimensions are clearly defined, the resulting metric and geometric morphometric data are minimally affected by inter-observer error, supporting this method as an effective solution for assessing error under collaborative research frameworks. Collaboration is becoming increasingly important within archaeological and anthropological sciences in order to increase the accessibility of samples, encourage dual-project development between foreign and local researchers and reduce the carbon footprint of collection-based research. This study offers a promising validation of a collaborative research design whereby researchers remotely work together to produce comparable data capturing lithic shape variability. Supplementary Information: The online version contains supplementary material available at 10.1007/s12520-022-01676-2.

Risk Factors and Predictors of Hypothermia and Dropouts During Open-Water Swimming Competitions.

PURPOSE: To measure core temperature (Tcore) in open-water (OW) swimmers during a 25-km competition and identify the predictors of Tcore drop and hypothermia-related dropouts. METHODS: Twenty-four national- and international-level OW swimmers participated in the study. Participants completed a personal questionnaire and a body fat/muscle mass assessment before the race. The average speed was calculated on each lap over a 2500-m course. Tcore was continuously recorded via an ingestible temperature sensor (e-Celsius, BodyCap). Hypothermia-related dropouts (H group) were compared with finishers (nH group). RESULTS: Average prerace Tcore was 37.5°C (0.3°C) (N = 21). 7 participants dropped out due to hypothermia (H, n = 7) with a mean Tcore at dropout of 35.3°C (1.5°C). Multiple logistic regression analysis found that body fat percentage and initial Tcore were associated with hypothermia (G2 = 17.26, P < .001). Early Tcore drop ≤37.1°C at 2500 m was associated with a greater rate of hypothermia-related dropouts (71.4% vs 14.3%, P = .017). Multiple linear regression found that body fat percentage and previous participation were associated with Tcore drop (F = 4.95, P = .019). There was a positive correlation between the decrease in speed and Tcore drop (r = .462, P < .001). CONCLUSIONS: During an OW 25-km competition at 20°C to 21°C, lower initial Tcore and lower body fat, as well as premature Tcore drop, were associated with an increased risk of hypothermia-related dropout. Lower body fat and no previous participation, as well as decrease in swimming speed, were associated with Tcore drop.