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Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the this http URL citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion. 

ABSTRACT We have not yet observed the epoch at which disc galaxies emerge in the Universe. While high-z measurements of large-scale features such as bars and spiral arms trace the evolution of disc galaxies, such methods cannot directly quantify featureless discs in the early Universe. Here, we identify a substantial population of apparently featureless disc galaxies in the Cosmic Evolution Early Release Science (CEERS) survey by combining quantitative visual morphologies of $${\sim} 7000$$ galaxies from the Galaxy Zoo JWST CEERS project with a public catalogue of expert visual and parametric morphologies. While the highest redshift featured disc we identify is at $$z_{\rm {phot}}=5.5$$, the highest redshift featureless disc we identify is at $$z_{\rm {phot}}=7.4$$. The distribution of Sérsic indices for these featureless systems suggests that they truly are dynamically cold: disc-dominated systems have existed since at least $$z\sim 7.4$$. We place upper limits on the featureless disc fraction as a function of redshift, and show that up to 75 per cent of discs are featureless at $3.0< z< 7.4$. This is a conservative limit assuming all galaxies in the sample truly lack features. With further consideration of redshift effects and observational constraints, we find the featureless disc fraction in CEERS imaging at these redshifts is more likely $${\sim} 29{\!-\!}38~{{\ \rm per\ cent}}$$. We hypothesize that the apparent lack of features in a third of high-redshift discs is due to a higher gas fraction in the early Universe, which allows the discs to be resistant to buckling and instabilities.