Fish habitat mapping using acoustic and GIS technologies
Bibliographic record
Abstract
A rapid and accurate the method of assessment for fish habitats is a growing need as there is increasing degradation of aquatic environments world wide, While several small Canadian companies have developed useful technology to assess fish habitats, their ability to manage and integrate data into geographic information system (GIS) is limited.The first objective of this thesis was to evaluate the methods for undertaking and assessing the acoustic classification of substrates for five freshwater systems ranging from a prairie river (Red River), to a Canadian Shield river (Winnipeg River), to a northem Canadian river (Mackenzie River), to small northem and Arctic lakes (Chitty Lake and Wormy Lake using a QTC VIEWTM and a QTC IMPACTTM (a package of hardware and software made by Quester Tangent Corporation).The Red River has few major substrate classes while the Winnipeg River has more numerous, rapidly changing, small-scale substrate pattems.The Mackenzie River, on the other hand, is a large river with lârge-scale homogeneous substrates allowing for the collection of many acoustic signals fiom one substrate type and making the correlation of acoustic signal and substrate classes easier.Ground truth was undertaken by collecting benthic samples and developing a "visual classification system" and a process for the separation of sediments based on grain-sizes and proportions.A geographical information system (GIS) was developed in which the acoustic data were used to interpolate into continuous bathymetry and subshate pattems.The second objective was to determine if a comprehensive catalogue of substrate classes could be developed, to coûelate them with the benthic samples, and to relate the bathymetry models and the substrate classes to fish movements, i.e. lake sturgeon, Acipenser fulvescenes,lake tro:ut Salvelinus namaycush and Arctic char Salvelinus alpinus, in a spatial context.To expand the understanding of substrate, the track points of lake sturgeon movements were superimposed on interpolated maps of the bathymetry and the substrate pattems at the Seven Sisters site on the Winnipeg River.The movements of lake sturgeon, lake trout and Arctic char were tracked in the three systems using a VEMCO's acoustic telemetry system.Additional selected samples of lake trout and Arctic char movement records (two fish of each species from each site) we¡e assessed to verifu the association of the fish movement and the substrate maps
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".