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Record W2106770588 · doi:10.1071/mf07247

Spatial and temporal patterns of habitat use by three estuarine species of mysid shrimp

2008· article· en· W2106770588 on OpenAlexaff
Matthew D. Taylor

Bibliographic record

VenueMarine and Freshwater Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsEcologyHabitatBiologyEstuaryMysidaceaPredationForagingTrophic levelPopulationDiel vertical migrationSeagrassCrustaceanFishery

Abstract

fetched live from OpenAlex

The mysids Rhopalopthalmus egregius, Haplostylus dakini and Doxomysis australiensis are abundant yet unstudied omnivorous crustaceans in Australian estuaries. Habitat use and population dynamics were investigated for these species over spring and summer in the Tweed River, Australia, to explore their ecological role in estuarine ecosystems. Overall, mysids were concentrated in shallow unvegetated and deep unvegetated estuarine habitats. H. dakini were most abundant in shallow and deep bare habitats at night, whereas R. egregius were most abundant in deep bare habitats during the night. D. australiensis were present across all habitats in the night, but negligible numbers were present during the day. Significantly greater numbers of R. egregius and D. australiensis were sampled during the new moon, compared with the full moon. Significantly larger R. egregius and D. australiensis individuals were present in benthic habitats at night, indicating possible partitioning of habitat for juvenile and adult subpopulations. Adaptive foraging strategies and habitat use facilitates the coexistence of sympatric mysid species, H. dakini and R. egregius, and within-species habitat partitioning allowed juvenile R. egregius to avoid interaction with adult R. egregius. The observed dynamics minimize inter- and intra-specific predation between mysids, and by other predators, while optimizing access to key trophic resources.

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 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 score0.070
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.056
GPT teacher head0.269
Teacher spread0.214 · 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

Citations21
Published2008
Admission routes1
Has abstractyes

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