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Antibiotics enter agro-ecosystems via the application of farmyard manure, sewage sludge, animal by-products, or digestates. There are many open questions regarding the behavior of such compounds in the soil like their adsorption, degradation, half-life, and their effects on soil organisms and plants. The impact of antibiotics on the development of antibiotic resistance genes in the environment is regarded as the most important effect that endangers the environment as well as human health. Nevertheless, direct plant toxicity, especially of different antibiotics and heavy metals at the same time, can be of importance as well. In the current study, commercially available phytotoxkits were tested with regard to the toxicity of single antibiotics and antibiotics in combination with the root growth of Sinapis alba L. Additionally, a pot trial was conducted to study the transfer of the observed phytotoxkits results in more complex systems. The phytotoxkits revealed direct toxicity of antibiotics on root development only at high concentrations. The highest toxicity was determined for sulfadiazine, followed by tetracycline and enrofloxacin, showing the least toxicity. When two antibiotics were tested at the same time in the phytotoxkit, synergistic effects were detected. The pot trial indicated lower effect concentrations for enrofloxacin than determined in the phytotoxkit and, therefore, to higher toxicity on plant growth.

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Machine-learning-based stress detection systems differ with respect to the ground truth used for training the algorithms. It is unclear how models trained on different facets of the stress reaction (e.g., biological, psychological, social) can be compared, interpreted and applied. In this study, we investigate the influence of the stress label on the performance of machine learning models trained on either vocal characteristics or facial expressions extracted from videos. We collected videos from 40 male participants while being exposed to the Trier Social Stress Test (TSST) and assessed selfreported, live observed, video-annotated and neuroendocrinological stress levels. We train three standard machine learning models to separately predict different stress labels using either voice or facial cues. Analyzing the relationships of different stress facets we found that observers' annotations were significantly positively associated (live vs. video annotated, ρs = .53). Similarly, the neuro-endocrinological stress indices correlated with each other (cortisol vs. sAA , ρs = .39). Machine learning experiments resulted in predictions that were positively associated with panel-annotated stress levels showing significantly stronger correlations in voice-based models (ρs = .54 vs. ρs = .30). Predictions of self-reported stress were positively related to ground truth values for face-based (ρs = .24) but not for voice-based models. There was no evidence for successful predictions of video-annotations or endocrinological stress levels in both settings. We provide evidence that machine learning models trained on different stress assessments perform differently and should be interpreted and applied accordingly. Implications and recommendations for future work on video-based stress detection are discussed.