Does the Lake Huron Shoreline Influence Distributions, Altitudes, and Flight Directions of Nocturnally Migrating Birds?
Bibliographic record
Abstract
An increase in the number of wind turbines caused by a developing need for renewable energy has led to concerns about their potential effects on birds and other wildlife.It is not well known whether turbines placed along shorelines may present a greater risk to migrating birds than turbines farther inland.I used five marine radars to test whether numbers and flight behaviours of migrating birds differed along a shoreline compared to inland.Radars were positioned at various distances from the Lake Huron shoreline along two transects, with one shoreline radar and one or two inland radars on each transect.Radars were operational between April 18 th and May 31 st 2014.The numbers of birds detected varied significantly among nights ranging between thousands to hundreds of thousands.There were small differences in the numbers of birds detected among sites, but no significant difference between shoreline and inland sites.The flight altitude varied significantly among nights, but there was no strong evidence that shorelines influenced the migratory flight altitude of birds.Flight direction varied among nights but within a night was generally similar among sites, with most birds on nights of heavy migration migrating north to north-east.The results from this study provide no evidence that placing wind turbines along the Lake Huron shoreline would present an increased risk of collisions to passing migrants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".