Preliminary investigation of bald eagle distribution, productivity and nest site requirements in Northern Ontario
Bibliographic record
Abstract
Bald eagles {Haliaeetus leucocephalus alascanus) have been declared an endangered \nspecies in Ontario. For protection of bald eagles from behavioural and habitat \ndisturbance, their nests are defined as Areas of Concerns by the Ontario Government. \nTo direct the management of these areas, the Ontario Ministry of Natural Resources \nreviewed the available literature and produced the Ontario Management Guidelines. \nHowever, the information available on Northern Ontario's bald eagles is limited, \nreferring to the Lake of tire Woods Area only. To study bald eagles and evaluate \nOntario's guidelines I gathered and analyzed data from across Northern Ontario on \nbald eagle habitat (1990, 1991, 1992), the effect of timber management on their \nreproduction (1990) and bald eagle distribution (1990). Data were analyzed \nunivariately and I developed logistic regression models for topographical, limnological \nand vegetation characteristics. Variables important for defining the probability of a \nnest occurring include lake dimensions, stand density and tire availability of super-dominant, \naccessible perch trees. Of the models developed, two had practical \nimplications: a limnological model which could be used to define potential foraging \nlakes and thus prevent unnecessary surveys and a vegetation model which could be \nused to evaluate habitat quality. Natality rates of bald eagles did not differ \nsignificantly among areas harvested according to guidelines, harvested without \nreference to the guidelines and undisturbed. The habitat features of forests, \nsurrounding Northern Ontario bald eagle nest sites are similar to elsewhere except for a \ngreater significance of perch trees. This justifies the Ontario Ministry of Natural \nResource's use of available data, but the guidelines may underestimate the number of \nlarge perch and nest trees in optimal bald eagle habitat.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".