Towards marine Geographic Information Systems: Multidimensional representation of fish aggregations and their spatiotemporal evolutions
Bibliographic record
Abstract
Global warming has deeply affected coastal and offshore marine ecosystems and is thought to have a significant impact on the fish stocks and their decline in the northwest Atlantic region in Canada. However, it is difficult for scientists and marine biologists to have a clear and precise insight of this impact. Studying fish aggregations and their evolution in time and space may help scientists to have a better understanding of the decline of fish stocks and elaborate potential solutions for it. Fish aggregations are not only 3D spatiotemporal phenomena which need to be represented and managed in 3D but also have fuzzy boundaries which makes it too difficult to clearly identify and delineate them in the space. Although, Geographic Information Systems (GIS) constitute a powerful tool for handling spatial information, they are prone to problems when dealing with 3D dynamic phenomena, especially, when those phenomena have fuzzy boundaries. This paper addresses these two problems at local and regional scales and proposes two new approaches for the representation and visualization of fish aggregations and their evolution through time and space. At the regional scale, the proposed approach combines fuzzy logic methods with spatial raster representation tools within GIS to provide a more realistic representation and visualization of fish aggregations and their evolution in time. At the local scale, we developed an integrated method based on 3D Delaunay triangulation and the 3D alpha-shapes algorithm to carry out the spatial modeling of fish aggregations in a true 3D space. The applications of these methods to the fisheries data reviled several potentials and limitations of the proposed methods which are discussed throughout this paper.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".