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Record W6959225485 · doi:10.7944/p95lq187

Evaluation of the Copper River Delta Dusky Canada Goose Survey and the Pacific Flyway's Management Index

2022· other· en· W6959225485 on OpenAlexaboutno aff

Bibliographic record

VenueFWS DOI Tool Production Environment · 2022
Typeother
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsnot available
Fundersnot available
KeywordsFlywayAerial surveyDeltaVegetation (pathology)Index (typography)WildlifeNest (protein structural motif)Wildlife management

Abstract

fetched live from OpenAlex

The Copper River Delta (CRD) in south-central Alaska is the primary breeding area for dusky Canada geese (Branta canadensis occidentalis). The Alaska Department of Fish and Game (ADFG), United States Forest Service (USFS), and United States Fish and Wildlife Service (USFWS) conduct breeding-ground surveys to inform the Pacific Flyway Council harvest management decisions and regulations. The current management index (described in detail below) uses aerial survey counts corrected for detection by the long-term average ratio of aerial counts to ground-based nest counts. Because of the 1964 earthquake that resulted in uplift and subsequent vegetation growth, the aerial detection of geese has been thought to decrease through time and the use of a constant correction factor is suspect. During the summer of 2020, personnel from the 3 agencies met to discuss the appropriateness of the index as it was currently calculated. The greatest concern identified was in the use of a constant detection correction derived from sparse historic data, and this report is an attempt to evaluate the components of the detection correction constant, as well as the management index in general, and provide potential alternatives going forward. Because the detection correction involves a ratio of the ground-based nest estimates to aerial-observed indicated pairs (hereafter 'ratio' or 'ground-to-air ratio'), change in the ratio could be due to change in either the numerator or denominator. Therefore, we evaluated methods and any change in methods or protocol associated with all survey components. Our goal is to evaluate the current design of surveys, the calculation of the management index, and to assess change in various components of the index, not to provide detailed recommendations for re-design of a survey. Such a re-design should take place in a separate effort after this evaluation, and after a general approach, perhaps different than the current one, is decided upon by the various stakeholders. Previous attempts at examining the ratio and change in ground-to-air ratio was limited to simple linear regression between air and ground point estimates calculated at the scale of each nest plot strata (Hodges 2007). This made the assumption that the relationship is linear, that the nest strata are the appropriate scale to calculate the ratio, that all point estimates are equally estimated, and a mean ratio across time is appropriate for calculating a correction factor. Because there is now more data available and more statistical techniques available to estimate the relationship between nests and aerially-observed geese, we also estimated the ratio at various scales and using different techniques to see if this leads to different conclusions. We were primarily concerned with annual variation in the ratio and distinguishing true process variation in this ratio from sampling variance (statistical noise). Current treatment of the ratio ignores annual (process) variation in the ratio, and we feel strongly that this is not appropriate if there is evidence for annual variation. Because annual variation in the ratio has never been estimated, we attempt to estimate this and apply it to the calculation of the management index.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.593

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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