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Record W2217794711 · doi:10.2989/00306525.2015.1089333

Using independent nest survey data to validate changes in reporting rates of Martial Eagles between the Southern African Bird Atlas Project 1 and 2

2015· article· en· W2217794711 on OpenAlexaboutno aff
Arjun Amar, Daniël Cloete, Madel Whittington

Bibliographic record

VenueOstrich · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyBreeding bird surveyNest (protein structural motif)WildlifeNational parkPopulationAerial surveyBiodiversityContext (archaeology)Survey methodologyQuarter (Canadian coin)EcologyDemographyCartographyStatisticsBiologyArchaeology

Abstract

fetched live from OpenAlex

Repeat monitoring is vital to measure biodiversity change. However, monitoring protocols may change, as survey techniques improve or different questions are asked. Such modifications may cause difficulties when examining changes in wildlife populations. The Southern African Bird Atlas Project (SABAP) 1 and 2 are repeat national bird surveys undertaken 20 years apart. These surveys therefore offer unrivalled potential to examine bird population changes in an African context. However, changes in protocols, both spatially and temporally, between the two surveys have raised concerns over using these data to infer population changes. In this study we use independ- ent nest survey data to test whether changes in reporting rates of Martial Eagles in the Kalahari Gemsbok National Park between the two SABAP surveys were reflected in real change in numbers of nesting pairs. From 11 quarter degree squares (QDS), covering c. 8 000 km2, both SABAP and nest surveys suggested a near identical 44% decline. Levels of agreement were weaker at the individual QDS scale, although in 67% of cases the direction of change was the same using both surveys. These results suggest that comparisons in the reporting rates between SABAP 1 and SABAP 2 accurately reflect changes in the breeding population size of this species.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.294
GPT teacher head0.359
Teacher spread0.065 · 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 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

Citations21
Published2015
Admission routes1
Has abstractyes

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