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Record W1994990064 · doi:10.5558/tfc85202-2

Bird monitoring programs in Ontario: What have we got and what do we need?

2009· article· en· W1994990064 on OpenAlexaffvenueabout
Charles M. Francis, Peter J. Blancher, R. Dean Phoenix

Bibliographic record

VenueThe Forestry Chronicle · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsEnvironment and Climate Change CanadaMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsAdaptive managementGovernment (linguistics)Environmental resource managementContext (archaeology)PopulationGeographyBird conservationBusinessEnvironmental planningEcologyHabitatBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Bird population monitoring should be designed to enhance conservation of birds through informing policy decisions and management actions. Many different bird surveys are undertaken in Ontario ranging from province-wide multi-species programs such as the Ontario Breeding Bird Atlas to single-species localized surveys for Species at Risk. Although most surveys provide some useful contributions towards understanding the status of bird populations, there remain significant gaps in both species and geographic coverages, especially in the northern half of the province, and few surveys are sufficient for evaluating the specific effects of current management practices on birds. Enhancing bird monitoring in the province should first involve clearly defining, quantitatively, the information required for management, conservation and decision-making, in the context of an adaptive management cycle, and then identifying the most cost-effective monitoring programs to obtain that information. This can most effectively be implemented through a cooperative effort involving all parties with an interest in bird monitoring data including federal and provincial government agencies, environmental non-government organizations, and industry. Key words: bird population monitoring, evaluation, adaptive management, decision-making

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.254
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2009
Admission routes3
Has abstractyes

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