Use of social network analysis to examine preferential co-occurrences in Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus Mitchill, 1815
Bibliographic record
Abstract
Abstract Background Application of Social Network Analysis (SNA) to acoustic telemetry is a useful approach to examine social behavior in fish. Atlantic Sturgeon ( Acipenser oxyrinchus oxyrinchus ) are ancient, long-lived anadromous finfish. Although Atlantic Sturgeon have been the subject of numerous telemetry studies, none have used SNA to analyze their co-occurrence behavior. During 2010–2014 Atlantic Sturgeon ( n = 103) that were later genetically identified as being from the Saint John River, Canada and the Kennebec River, US were captured by otter trawl and brush weir in Minas Basin, Bay of Fundy, Canada, and acoustically tagged. Using data from moored acoustic receivers within foraging habitat in Minas Basin, we tested if Atlantic Sturgeon formed social associations that were random or structured during 2012 to 2014; and whether these co-occurrences consisted of individuals from the same river of origin or capture date. Results Irrespective of genetic origin and initial capture date, Atlantic Sturgeon formed co-occurrences in Minas Basin that were significantly different than would be observed by chance during 2012 and very close to significant during 2013. Analysis demonstrated that some Atlantic Sturgeon preferentially co-occur within their primary feeding habitat. Conclusions The current threats to Atlantic Sturgeon aggregations within the Bay of Fundy, Nova Scotia, include bycatch in fisheries throughout their coastal migration routes and more recently the development of tidal turbines along their migratory corridor to their summer aggregation site. It is important to determine if Atlantic Sturgeon form aggregations with conspecifics from the same population to inform management decisions regarding threats to groups of individuals. This study indicated that Atlantic Sturgeon may form preferential co-occurrences within their feeding aggregation and co-occurrences that were identified were not dependent upon population of origin or initial capture date.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".