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Record W6892154408 · doi:10.5063/f13r0r59

Mountain Birdwatch 2.0: 2010-2018

2019· dataset· en· W6892154408 on OpenAlexaboutno aff

Bibliographic record

VenueUC Santa Barbara · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSampling (signal processing)PopulationStratified samplingDistance samplingBreedHabitat

Abstract

fetched live from OpenAlex

Mountain Birdwatch (MBW) 2.0 is a long-term citizen science monitoring program for 10 bird and 1 mammal species that breed in high-elevation spruce-fir forests of the northeastern United States and, formerly, southeastern Canada. Initiated in 2000 as Mountain Birdwatch 1.0, MBW provides the only region-wide source of population information on these high-elevation breeding birds. Each June, under the coordination of the Vermont Center for Ecostudies, volunteers perform repeated point counts at nearly 750 long-term fixed sampling sites along hiking routes in Vermont, New Hampshire, Maine, and eastern New York. Primary emphasis was placed on Bicknell’s Thrush, a montane-fir specialist that breeds only in the Northeastern U.S. and adjacent portions of Canada. In 2010, the program underwent many positive changes to reemerge as Mountain Birdwatch 2.0. All of the sampling locations prior to 2010 were permanently abandoned, and new sampling locations were chosen using a generalized random tessellation stratified (GRTS) procedure. For more information see: https://vtecostudies.org/projects/mountains/mountain-birdwatch/

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.578

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 head0.030
GPT teacher head0.289
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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