How small can they go? Microelectronic tags for movement ecology of small aquatic organisms
Bibliographic record
Abstract
Abstract Miniaturization and optimization of batteries and electric components, as well as new technological innovations, are driving increased use of microelectronic tags to study animals in the wild that are smaller than ever before. Here, we provide an overview of the different alternatives to common electronic tagging and tracking tools used for aquatic research and discuss the research opportunities afforded by these micro tags and the challenges for investigators. We are optimistic that the miniaturization of tags will create opportunities for novel ecological inquiry. A key advance will be to allow investigators to address broader questions at an ecosystem scale about aquatic environments that span small-bodied adult fishes and life stages (i.e., juveniles). However, even the new developments have limitations in what can be tagged, how long tags will last, and their detection distance. Moreover, investigators will need to better understand how to effectively instrument the smallest animals with surgical implants or attachments of tags to maintain fish welfare and minimize alterations of behavior or survival. Collaboration with engineers will be important to assess where the field can go next for miniaturization, which will help to further advance the understanding of small species and early life stages in rivers, lakes, estuaries, and oceans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".