Bibliographic record
Abstract
With billions of individuals and possibly hundreds of thousands of genera, invertebrates represent the largest number and greatest diversity of all animals used in research. Although the capacity for nociception is recognized in many invertebrate taxa, researchers and IACUC members are challenged by a lack of clear understanding of invertebrate welfare and by differing standards of moral concern for these taxa. In practice this has led IACUCs to consider invertebrates in two major groups: species worthy of increased moral concern approximating that shown to vertebrate species (this group includes cephalopods and to some extent decapod crustaceans) and all others. This dichotomy has led to differences in how invertebrate research is regulated and documented. This article presents two case studies illustrating specific concerns in invertebrate research protocols and then provides relevant information to address practical IACUC matters related to regulatory and ethical issues, sourcing and record keeping, risk management, assessment of pain and nociception in invertebrates, housing and husbandry, invasive procedures, veterinary care, and humane endpoints.
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 | 0.416 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.014 | 0.020 |
| Research integrity | 0.030 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".