From Corpus to Canon: An LLM-Assisted Screening Algorithm and a Multi-Scale Unification of Biological-Floc Modelling Equations

Keeping a quantitative review current in a fast-growing applied field is first a screening problem. We present two algorithms for modelling biofloc aquaculture. The first, Floccus, is an openly archived, PRISMA 2020-compliant pipeline that removes duplicates in three layers (deterministic, embedding-based and large language model (LLM)-judged) and screens each record against eleven criteria under an asymmetric decision rule with an auditable trail. Applied to 22,056 records from four databases, it yielded 16,965 unique and 4883 eligible studies in 25 thematic clusters. Agreement with blind human labels was low (κ=0.107), of the same order as between two human reviewers applying very different inclusion thresholds (κ=0.105; 65% versus 18% of records judged relevant); it saved more screening work than keyword and single-prompt baselines, and an aquatic-science database added under 1% of eligible studies. The second algorithm assembles the corpus’s six model families into one multi-scale hierarchy coupled through the aeration-driven dissipation field, with a proposed operator converting floc size into active biomass through the Thiele effectiveness factor. Manual screening confirmed formal floc modelling is rare in applied biofloc work (22 of 141 records). Corpus-level recall remains moderate (0.45), and the coupled model awaits validation against tank data.