Spawning Habitat Selection and Egg Deposition by Reintroduced Lake Sturgeon in a Tributary to Cayuga Lake, NY
Bibliographic record
Abstract
In June 2017, we documented the first observed spawning event by a reintroduced population of Lake Sturgeon (Acipenser fulvescens) in Fall Creek, a tributary to Cayuga Lake, New York, USA. This is the first observed spawning encounter of adult Lake Sturgeon since the beginning of the multi-agency Lake Sturgeon restoration effort in Cayuga Lake initiated in 1995 by the New York State Department of Environmental Conservation. Lake Sturgeon egg deposition was found specifically on substrate mainly composed of gravel sized rocks with depths and flows that made up a unique microhabitat combination within the creek which is not typical of habitat identified in other Lake Sturgeon spawning habitat studies across the Great Lakes. An estimated 810,052 ± 24,386 eggs were deposited in the sampled area of Fall Creek. The identified, potentially productive spawning microhabitat type in Fall Creek is likely to be widespread in similar tributaries around Cayuga Lake as well as small tributaries to other Finger Lakes and Lake Ontario. Ongoing research is focused on the evaluation of the extent of Finger Lakes habitat similar to that identified in Fall Creek. This microhabitat evaluation of sturgeon spawning and the broad scale landscape knowledge of tributary habitats, should support subsequent management to restore Lake Sturgeon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".