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Record W7147340836 · doi:10.5281/zenodo.19356532

Pluvialis squatarola

2018· article· W7147340836 on OpenAlexaboutno aff
Marina Somenzari, Priscilla Prudente do Amaral, Víctor R. Cueto, André de Camargo Guaraldo, Alex E. Jahn, Diego Mendes Lima, Pedro Cerqueira Lima, Camile Lugarini, Caio Graco Machado, Jaime Martínez, João Luiz Xavier do Nascimento, José Fernando Pacheco, Danielle Paludo, Nêmora Pauletti Prestes, Patrícia Pereira Serafini, Luís Fábio Silveira, Antônio Emanuel Barreto Alves de Sousa, Nathália Alves de Sousa, Manuella Andrade de Souza, Wallace Rodrigues Telino Júnior, Bret Myers Whitne

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Language
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsTundraPluvialisPeninsulaPeriod (music)Historical record

Abstract

fetched live from OpenAlex

Pluvialis squatarola (MGT): breeds in the Arctic, in the tundra in northern Canada, Alaska and Russia. It leaves its breeding areas heading south between July and September, and juveniles depart 5 to 6 weeks after adults. In general, this species reaches the coast of Guyana and then the Gulf of Maranhão during fall migration. The return from its wintering areas (coasts of North, Central and South America) occurs between April and mid-May (Wiersma, 1996). Although there are localized records in November for the Marchantaria and Anavilhanas Islands (Stotz et al., 1992), most records are associated with coastal areas throughout the Brazilian coast and are centered in the period between September and May (WikiAves, 2016; MZUSP; MPEG; MNRJ).There are records in all months of the year for RS, but the species is recorded mainly from September to April in this state (Belton, 1994).

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.033
GPT teacher head0.251
Teacher spread0.218 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicAvian ecology and behavior→French-language works237,207→