Feature: Fish Habitat - Quantifying submerged aquatic vegetation using aerial photograph interpretation: Application in studies assessing fish habitat in freshwater ecosystems
Bibliographic record
Abstract
Use of aerial photograph interpretation (API) in resource inventory projects recently has increased, and this reflects benefits like established protocols, high spatial resolution, readily available photography, and limited cost. Application of API to quantify features of aquatic habitats used by fishes, like submerged aquatic vegetation (SAV), has been advocated for decades but a paucity of use suggests inadequate awareness of the methods. This article reviews a protocol that guides the use of API to quantify features of aquatic habitats, and then uses examples from contrasting habitats in the Lake Ontario watershed from 1972–2003 to illustrate this protocol. Even though we used photographs originally collected for other purposes, API identified the change in minimum area and depth distribution of SAV over time. These observations reinforce how API can contribute information to resource inventories, and why investigators should expand use of API in studies of aquatic ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".