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Record W2903841932 · doi:10.1590/1982-0224-20170003

Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil

2018· article· en· W2903841932 on OpenAlexfundno aff
Hersília de Andrade е Santos, Cecília Gontijo Leal, Paulo dos Santos Pompeu, Ceceo Chaves, Stephanie Fernandes Cunha

Bibliographic record

VenueNeotropical Ichthyology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsHabitatBiologyEcologyFish <Actinopterygii>CharacidaeFishery

Abstract

fetched live from OpenAlex

ABSTRACT Physical habitat simulation (PHABSIM) is an important step of the instream flow incremental methodology (IFIM), which is applied to establish environmental flow regimes. This study applied the PHABSIM in two reaches of the Velhas river basin, whose long-term discharges are similar but are under different degrees of impact. Suitability curves were obtained for fish species using traditional methods (Astyanax sp., Piabarchus stramineus, Piabina argentea and Serrapinnus heterodon) and generalized additive models for fish density (Astyanax sp., P. argentea and S. heterodon). The results of habitat use depended on the method for curves generation. Applying the suitability curves by traditional methods, different discharge scenarios were simulated. The flow increasing from a dry scenario to a discharge of 1 year of return promotes a possible habitat increase for all species. However, the same hydrological flow percentiles produce different habitat proportions in different rivers. This work demonstrates that regardless of how suitability curves for the Neotropical species are generated, caution should be taken when applying them. However, the PHABSIM method allows more complex analyses than the traditional approaches based on minimal flow estimations, which is usually applied in South America.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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