Correction: Perceptual consensus on cross-country ski–snow performance: a questionnaire study of experts and non-experts

At the systems level, cross-country (XC) skiing performance reflects the balance between propulsive and resistive forces. Under typical race conditions, ski-snow friction and aerodynamic drag account for approximately 30% and 15%, respectively, of the energetic cost (1). The relative importance of these losses varies with the type of skiing, i.e., classic or skate, sub-technique execution, weather conditions, terrain segment, and speed (2,3,4,5,6). Ski-snow friction tends to dominate resistive forces at lower speeds and during uphill work, whereas aerodynamic drag becomes increasingly important on flats and downhills as speed increases. Furthermore, the relative contribution of these forces differs between classic and skating sub-techniques due to variations in posture and speed profiles (7,8). Even small reductions in ski-snow friction can therefore yield meaningful performance gains. For example, a power-balance simulation of XC skiing showed that reducing the friction coefficient from 0.037 to 0.030 shortened a 4.2 km race by 48 s (9,2), corresponding to roughly 1.6 s per kilometre for each 0.001 decrease in friction. These sensitivities highlight why glide optimisation is central to skiing performance.In practice, glide-optimisation decisions are also made in both training and competition contexts, where available time, fatigue, and feedback quality can differ substantially. Moreover, optimisation of glide is not merely a technical task, but a systemic one. Already in 1980, Ekström characterised XC skiing as a "relationship between man, equipment and environment" in which all factors must be tuned to one another for an optimal result (10). In practical terms, this framing emphasises the "human factor", i.e., the technician's and athlete's ability to perceive environmental cues and adapt equipment accordingly. This ability serves as a decisive filter that links physical conditions to realised performance (11,12).Much of ski preparation is informed by subjective glide testing. Such testing is central but inherently sensitive to factors such as fatigue, motivation, pacing, and rapidly changing micro-conditions, which can confound perceived differences. At the same time, advances in measurement technology, e.g., differential GNSS/IMU approaches for on-snow performance analysis (13,14) and tribometer-based quantification of ski-snow friction (15,3,4,16), create opportunities to complement subjective assessment with more objective feedback and decision support under variable conditions.The physical mechanisms governing glide and grip have been extensively studied within tribology, where ski-snow friction is known to depend on snow microstructure, water film formation, snow deformation, ski-base topography, and camber mechanics (17,18,19,20,21,22,23,24,25,26,27,28). Recent advances have clarified how snow type, grain size, temperature, and contamination interact with ski structure, wax, and hand structuring to influence frictional behaviour (29,30,31,19,11,22,32). These insights underpin modern ski preparation practices and influence equipment choices across all performance levels (30,11).Despite the technical foundation, ski preparation in practice relies not only on physical principles but also on empirical knowledge developed by skiers and ski technicians (11,30). Skiers and ski technicians routinely make judgements about structure choice, wax typesfoot_0 and hardness, the relative skiing speed on different snow types, and how factors such as body mass or positioning affect glide (31,23,24,26). Such decisions play a decisive role in both training and competitive environments, yet empirical evidence on how the skiing community perceives these factors is sparse (11).Earlier work in sports science has shown that practitioners often apply heuristic reasoning under uncertainty (33,34), and that expert decision-making can differ systematically from that of recreational participants (35). In skiing, technical expertise in ski preparation is typically acquired through repeated exposure to diverse snow conditions and detailed feedback during waxing and testing (36,11). Despite this, the degree of consensus regarding key determinants of ski performance remains largely unquantified.It is unclear whether consensus exists regarding perceived ski speed on common snow types, how skiers perceive the influence of weight or positioning on glide, or how they prioritise different ski properties when aiming to optimise performance.Ski preparation is further challenged by snow variability, rapidly changing temperature and humidity, and limited testing time. For example, even well-resourced teams encountered significant difficulties under atypical warm/wet conditions during the Sochi 2014 Winter Olympics. Understanding these perceptions is relevant for three reasons:• Agreement reflects shared mental models within the skiing community. High consensus suggests that practitioners converge on similar interpretations of snow behaviour, whereas low consensus indicates divergent conceptualisations that may influence preparation strategies (36,35).• Quantifying perception differences between experts and non-experts provides insight into how experience shapes understanding of ski-snow interaction. Such knowledge may inform technician training, support coach-technician communication, and improve knowledge transfer between elite and recreational environments (35,33).• Identifying areas of low agreement can expose domains where intuition deviates from physical evidence, guiding targeted educational interventions and highlighting priorities for future experimental research (34,11).To address these gaps, we conducted a nationwide questionnaire study to examine how Swedish skiers and ski technicians evaluate snow types, the determinants of glide performance, and ski preparation strategies.We quantified within-group agreement and between-group differences using statistical methods tailored for ordinal data (37,38,39). Specifically, we sought to answer three research questions:1. To what extent do skiers and ski technicians agree on key questions relevant to ski choice, waxing strategy, and skiing performance? 2. Does consensus vary across snow types?This study provides the first systematic mapping of perceptual consensus on ski-snow interaction across a broad sample of the skiing community. The findings clarify where experiential knowledge aligns with established physical mechanisms and where it diverges, offering a foundation to strengthen evidence-based preparation strategies and guide future applied tribology research.A cross-sectional online questionnaire was distributed to Swedish skiers, coaches, and ski technicians at all levels between October 13, 2021, and January 31, 2022. The questionnaire link was disseminated through local ski clubs, regional skiing networks, and public sport-related social media channels. Participation was voluntary and anonymous, and no incentives were offered. Respondents were eligible if they were active skiers (classic and/or skate) and at least 15 years of age. No additional exclusion criteria were applied. In accordance with the Swedish Ethical Review Act, this study did not require ethical approval as it involved only questionnaire data collection with no processing of personal information, no physical intervention, and no methods designed to physically or psychologically affect the participants. Participants provided informed consent via the survey platform and could withdraw at any time prior to submitting the questionnaire by closing the survey window.A total of 249 participants completed the questionnaire. We categorised participants as experts (n = 20, 8%) if they met the following criteria:• Over 500 ski preparations per year.• Over 1000 km of skiing per year.• Reported a role as waxer.The remaining participants were classified as non-experts (n = 229, 92%). This classification was established post-hoc in consultation with national team ski technicians from XC skiing and biathlon to ensure a sufficiently high threshold was established to be considered an expert.The questionnaire addressed various aspects of ski preparation and glide perception across eight distinct snow conditions. The snow conditions were classified according to SWIX's snow classification system (as of October 13, 2021), presented with icons in Table 1. Note that this system has been refined since the questionnaire administration. The list of questions and answer options is detailed in Table 2.Table 1. The questionnaire's snow types ST1-ST8, and the correspondence to SWIX's nine field types as icons (adapted and recolored from the originals). ST8 Very wet corn snow Saturated corn/slush.Participant characteristics and questionnaire responses were summarised and stratified by expert group.Descriptive statistics were presented as frequencies and percentages for categorical variables, and medians with interquartile ranges for continuous variables. To quantify the level of consensus among participants, Note. Questionnaire items were developed iteratively-in Swedish-by the author team in consultation with experienced trainers, skiers, team leaders, and XC waxing practitioners, including personnel from the Swedish Olympic Committee, the waxing and ski service teams of the Swedish Ski Association (SSF), and the Swedish Biathlon Association (SSSF). This expert input informed the study design, item selection, and wording. Items were subsequently pilot checked to confirm terminology and response-option clarity. For the present article, the original Swedish questions and answer options were translated into English; the wording shown here is the authors' translation of the Swedish questionnaire used for data collection.we calculated the agreement coefficient A (39), for each question and snow type combination using the agrmt package in R, (40).van der Eijk's agreement coefficient A is designed explicitly for ordered rating data. It ranges from -1(complete polarisation: responses concentrated at opposite ends) to +1 (complete agreement: responses concentrated in a single category), with 0 indicating a uniform distribution across categories. This measure addresses limitations of traditional agreement indices by accounting for the number of response categories Kalén et al.and avoiding the assumption of uniform distributions (39). Agreement was calculated separately for the overall sample, the expert group, and the non-expert group.To compare responses between expert and non-expert groups, we calculated Cliff's δ, a non-parametric effect size measure appropriate for ordinal data (37) A total of 249 participants completed the questionnaire. The sample consisted predominantly of middleaged males (40-59 years, n = 149, 60%). Regarding activity levels, the majority of respondents reported low frequencies of ski preparation (