Fallers
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Use axes or chainsaws to fell trees using knowledge of tree characteristics and cutting techniques to control direction of fall and minimize tree damage.
The occupation "Fallers" has an automation risk of 19.3%, indicating a relatively low likelihood of being fully replaced by automated systems in the near future. This low base risk, closely aligned with the calculated base rate of 19.4%, is largely due to the unique combination of physical demands, situational awareness, and decision-making required in this job. Fallers are responsible for using chainsaws or axes to cut down trees, a task that involves adapting to unpredictable environmental conditions and real-time assessment of hazards. While technology in forestry is advancing, the sheer variety of situations encountered in different forests, terrains, and weather conditions makes full automation challenging. The most automatable tasks for Fallers involve routine, predictable, or highly repetitive actions. For instance, tasks such as stopping saw engines, pulling cutting bars from cuts, and running to safety as the tree falls could be automated with specialized machinery and robotics, especially in controlled forestry environments. Similarly, appraising trees for certain visible or sensor-detectable characteristics (like rot or lean) and sawing back-cuts for controlled falls are tasks that could benefit from advancements in machine vision and precision sawing equipment. These processes lend themselves to automation because they are based on technical skills that can be replicated by specialized hardware and software, given the appropriate environment. However, several critical tasks performed by Fallers resist automation due to the necessity for human judgment, teamwork, and adaptability. Placing supporting limbs or poles to prevent splitting and rolling requires hands-on adjustments often dictated by the immediate situation and terrain, which are hard for machines to interpret without extensive sensory input and adaptive algorithms. Working as part of a team—coordinating between chainsaw operation and skidder operation—requires a level of communication and real-time role-switching that is challenging for current AI. Marking logs for identification is also resistant to automation, as it often involves visual assessment and contextual decision-making. Bottleneck skills such as Originality, with observable levels of 2.1% and 1.8%, further reinforce this resistance; generating creative solutions to novel, rapidly changing problems is still an area where human workers significantly outperform machines in forestry settings.