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Record W2896937972 · doi:10.1590/1982-0224-20180019

Flowing into the unknown: inferred paleodrainages for studying the ichthyofauna of Brazilian coastal rivers

2018· article· en· W2896937972 on OpenAlexaff
Andréa T. Thomaz, L. Lacey Knowles

Bibliographic record

VenueNeotropical Ichthyology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEndemismPleistoceneContinental shelfBathymetryGeographyOceanographyGeologyFisheryPaleontologyBiology

Abstract

fetched live from OpenAlex

ABSTRACT The eastern coastal basins of Brazil are a series of small and isolated rivers that drain directly into the Atlantic Ocean. During the Pleistocene, sea-level retreat caused by glaciations exposed the continental shelf, resulting in enlarged paleodrainages that connected rivers that are isolated today. Using Geographic Information System (GIS), we infer the distribution of these paleodrainages, and their properties for the east Brazilian coast. Specifically, using elevation/bathymetric data for the largest sea-level retreats during the Pleistocene, the paleodrainages, their area and the number of contemporary basins connected by each palaeodrainage, was inferred. For the 145 inferred paleodrainages, total paleodrainage area is strongly correlated with the contemporary area encompassed by each paleodrainage, as well as with the number of contemporary basins encompassed by a paleodrainage. Differences in the continental shelf exposure along the coast affected the degree of past connectivity among contemporary rivers. With our results freely available, we discuss how paleodrainages have tremendous utility in biological studies, especially in regions with limited geologic data. With respect to the diverse ichthyofauna of the Brazilian coast, and its high endemism, we highlight how the inferred paleodrainages provide a backdrop to test hypotheses about the effect of past riverine connectivity on diversity patterns.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.021
GPT teacher head0.280
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; a candidate call from one teacher head, not a consensus.

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

Citations57
Published2018
Admission routes1
Has abstractyes

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