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Record W1862799318 · doi:10.22230/jem.2009v10n1a407

New methods for assessing Marbled Murrelet nesting habitat: Air photo interpretation and low-level aerial surveys

2009· article· en· W1862799318 on OpenAlexaff
Alan E. Burger, F. Louise Waterhouse, Ann Donaldson, Carolyn Whittaker, David B. Lank

Bibliographic record

VenueJournal of Ecosystems and Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsVictoria General HospitalGovernment of British ColumbiaSimon Fraser UniversityUniversity of Victoria
FundersMinistry of Environment
KeywordsNesting (process)GeographyHabitatAerial surveyFisheryEcologyEnvironmental resource managementEnvironmental scienceCartographyEngineering

Abstract

fetched live from OpenAlex

This extension note summarizes the application of two new methods that were developed to assess the quality of forest habit that Marbled Murrelets (Brachyramphus marmoratus) use for nesting in British Columbia: air photo interpretation and low-level aerial surveys. Both methods use comparable sixlevel ranking systems that are based on the availability of forest attributes deemed important for nesting murrelets. The methods were developed and refined through preliminary work done in many varied coastal regions in British Columbia; they were designed to complement each other and be applicable to either small patches (1-2 ha) or to larger polygons used in mapping for forest management. Both methods were tested in comparisons with known murrelet nest sites and both are currently being applied by government and forest industry biologists. This note provides practitioners who are proposing to use one or both of these methods a concise guide to their suitability and limitations, and also provides links to relevant reports that offer greater detail on testing and applicability.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.325
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations8
Published2009
Admission routes1
Has abstractyes

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