Commentary: comparing efficiency in aquatic and terrestrial animal production systems
Bibliographic record
Abstract
First paragraph: Aquaculture is receiving increased attention from a variety of stakeholders. This is largely due to its current role in the global food system of supplying more than half of the seafood consumed, and also because the industry continues to steadily expand (UN Food and Agriculture Organization 2018). A recent article in Environmental Research Letters, 'Feed conversion efficiency in aquaculture: do we measure it correctly?', by Fry et al (2018a) found that measuring feed conversion efficiency of selected aquatic and terrestrial farmed animals using protein and calorie retention resulted in species comparisons (least to most efficient) and overlap among species dissimilar from comparisons based on widely used weight-based feed conversion ratio (FCR) values. The study prompted spirited discussions among researchers, industry representatives, and others. A group assembled to write a standard rebuttal, but during this process, decided it was best to engage the study's original authors to join the discourse. Through this collaboration, we provide the resultant additional context relevant to the study in order to advance conversations and research on the use of efficiency measures in aquatic and terrestrial animal production systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.055 | 0.065 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".