We developed an empirical weight prediction model for juvenile Chinook Salmon Oncorhynchus tshawytscha ranging from 33–113 mm FL and 0.35–14.86 g using morphometrics analysis on images that were collected from live fish in the field. This method relies on consumer‐grade hardware and free software and addresses the issue of a lack of valuable weight and condition data for life history and survival modeling. A blind test revealed that the method was relatively precise, with a mean absolute error of 0.075 g and a mean absolute percentage of error of 2.86%, and unbiased, with a mean error of −0.001 g. Given the inherent variability in field wet‐weight measurements that are estimated, with an average standard deviation of 0.030 g and CV of 1.02% from a blind repeated‐measurements experiment, the observed level of predictive error suggests that the method is an adequate substitute for measuring field weight when conditions (i.e., boat sampling) preclude conventional scale taring and accurate weight measurement. Additionally, this method is useful in an automated data‐quality‐control workflow to identify erroneous length and/or weight measurements. The image landmark placement closely follows previous work on juvenile Chinook Salmon and can be incorporated with existing methodologies such as stock analysis and phenotypic differentiation to increase the available biometric information.

中文翻译：

---

基于图像形态计量学的奇诺克鲑鱼幼鱼体重预测

我们开发了经验丰富的体重预测模型的奇努克鲑鱼Oncorhynchus tshawytscha对从田间活鱼采集的图像进行形态计量学分析，范围从33–113 mm FL和0.35–14.86 g。这种方法依赖于消费者级别的硬件和免费软件，并解决了缺乏用于生命史和生存模型的有价值的体重和状况数据的问题。盲测表明该方法相对准确，平均绝对误差为0.075 g，平均绝对误差百分数为2.86％，无偏倚，平均误差为-0.001 g。给定估计的现场湿重测量固有的可变性，通过盲法重复测量实验得出的平均标准偏差为0.030 g，CV为1.02％，观察到的预测误差水平表明该方法可以替代在条件（例如，船采样）排除了常规的称重皮重和精确的重量测量。此外，此方法在自动数据质量控制工作流程中很有用，可识别错误的长度和/或重量测量值。图像标记的放置紧跟先前在少年奇努克鲑鱼上的工作，并且可以与现有方法（例如股票分析和表型分化）结合使用，以增加可用的生物特征信息。