Knock down to level up: Reframing RNAi for invertebrate ecophysiology
Bibliographic record
Abstract
Abstract: Comparative ecophysiologists strive to understand physiological problems in non-model organisms, but the use of molecular tools that manipulate gene expression such as RNA interference (RNAi) are under-used in our field. Here, we provide a framework for invertebrate ecophysiologists to use RNAi to answer questions focused on physiological processes, rather than as a tool to investigate gene function. We specifically focus on non-model invertebrates, in which the use of other genetic tools (e.g., genetic knockout lines) are less likely. We argue that because RNAi elicits a temporary manipulation of gene expression, and resources to carry out RNAi are technically and financially accessible, it is an effective tool for invertebrate ecophysiologists. We cover the terminology and basic mechanisms of RNA interference as an accessible introduction for “non-molecular” physiologists, include a suggested workflow for identifying RNAi gene targets and validating biologically relevant gene knockdowns, and present a hypothesis-testing framework for using RNAi to answer common questions in the realm of invertebrate ecophysiology. This review encourages invertebrate ecophysiologists to use these tools and workflows to explore physiological processes and bridge genotypes to phenotypes in their animal(s) of interest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".