Advances in remote sensing of freshwater fish habitat: A systematic review to identify current approaches, strengths and challenges
Bibliographic record
Abstract
Abstract Remote sensing technology offers the ability to derive information on freshwater fish habitats across broad geographic areas and has the potential to transform approaches to monitoring. However, the numerous platforms, sensors and analytical software that are available may overwhelm those interested in utilizing this important technology and thus limit its broad application and uptake. Our review is intended to shed light on the capacity of this technology to transform freshwater fish habitat monitoring by examining the fundamental characteristics of the major remote sensing technologies that have been used for characterizing freshwater habitats, conducting a systematic literature review of studies that have used remote sensing technologies to characterize freshwater fish habitats and, highlighting some of the key habitat features, fish species and regions, that have been examined. Lastly, we identify the relative strengths and weaknesses of the various remote sensing technologies that can be used, recommend future research that could help improve the use of these technologies, and, provide a series of important considerations for those who are interested in utilizing these technologies for freshwater fish habitat characterization.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".