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Record W2187401671 · doi:10.11646/zoosymposia.5.1.17

<p class="HeadingRunIn"><strong>The effect of a summer flood on the density of caddisfly (Trichoptera) in the middle reaches of the Shinano River, Japan</strong></p>

2011· article· en· W2187401671 on OpenAlexfundno aff
Goro Kimura, Kimio Hirabayashi

Bibliographic record

VenueZoosymposia · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
FundersUniversity of TorontoMinistry of Land, Infrastructure and TransportMinistry of Education, Culture, Sports, Science and Technology
KeywordsCaddisflyInstarFlood mythBenthic zoneLarvaPopulation densityFloodplainEcologyPopulationBiologyGeographyDemographyArchaeology

Abstract

fetched live from OpenAlex

We investigated the response of caddisfly species assemblages in the middle reaches of the Shinano River to a flood that occurred in mid July 2006. Prior to the flood (on Day -22) the population density of total benthic caddisflies was 8,266.7 ± 2,392.1 individuals m-2. After the flood, by Day 11, the population density had decreased to 55.6 ± 55.6 individuals m-2. By Day 65, the density of caddis species had recovered to nearly the same level as that recorded before the flood, particularly in the case of Hydropsyche orientalis Martynov, the most abundant benthic species. By Day 65 the larvae of this species had reached pre-flood levels. On Day 40 it was noted that the larval population was dominated by final instars, but by Day 65 early instar larvae were dominant and downstream drift consisted mainly of second instars. Moreover, H. orientalis adults were constantly collected during the study period and the abundance of adults increased after Day 50. These results suggested that drift and reproduction were the main recolonization mechanisms that contributed to the rapid recovery of benthic caddisfly after the flood.

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)0.002
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.190
Teacher spread0.168 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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