Opportunities for Improving Aquatic Restoration Science and Monitoring through the Use of Animal Electronic-Tagging Technology
Bibliographic record
Abstract
The ecological effectiveness of widespread and costly aquatic restoration efforts is often unknown. We reviewed studies incorporating electronic-tagging techniques (including radio, acoustic, satellite, biologging, and passive integrated transponder tags) into restoration-monitoring programs and discuss novel uses of these technologies and experimental design considerations. We found 25 studies, mostly published after 2005. Most were focused on salmonids or monitored the residency of species at artificial reefs. Few studies used site-level replication or data collected prior to restoration or at control sites, which limits the usefulness of their results for evaluating restoration effectiveness. The use of electronic tags and related sensors (e.g., temperature, depth) can reveal how habitats are used and their associated bioenergetic costs or benefits. These technologies are focused on individual- and population-level responses and complement traditional methods of assessing abundance, richness, and community composition but must be deployed in conjunction with well-designed experiments to truly better inform evaluations of restoration effectiveness.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".