The potential of oviduct tags and fine‐scale acoustic telemetry to reveal the timing and location of spawning in <scp>Arctic</scp> salmonids (<i>Salvelinus</i> spp.)
Bibliographic record
Abstract
Identifying and characterizing spawning locations are paramount for the protection of critical fish habitats but can be challenging, particularly in remote locations. Using the underexplored oviduct-tagging technique, we aimed to identify the timing and location of spawning for wild Arctic char (Salvelinus alpinus) and lake trout (Salvelinus namaycush) in two high-Arctic lakes in Nunavut. Specifically, Innovasea V7 acoustic telemetry transmitters were inserted into the oviducts of 13 Arctic char and 4 lake trout, and the timing and location of tag expulsion were determined using a fine-scale positioning system. Twenty Arctic char and 20 lake trout were also tagged with abdominal V16 transmitters, and 10 of them were paired with the oviduct tags, to further study the behavior of individual fish during the spawning season. Oviduct tags from four Arctic char and one lake trout could be used to assess the timing and location of spawning. Spawning anadromous Arctic char drastically reduced their activity and remained proximate to their presumed spawning location immediately before and for months after spawning. In contrast, a non-anadromous (i.e., freshwater resident) Arctic char and a lake trout showed little to no reduction in activity around presumed spawning events. Because of the highlighted sedentary behavior of inferred spawning anadromous Arctic char implanted with both abdominal and oviduct tags, we could also infer potential spawning based on the behavior of individuals equipped only with abdominal tags. Spawning areas identified via telemetry also aligned well with Inuit knowledge of those lakes. This is the first field study to use acoustic oviduct and abdominal tags coupled with a fine-scale positioning system. Despite a limited success rate of ejection, the study reveals the strong potential of the method to study spawning habitat and timing, particularly in remote areas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".