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Record W6907556055 · doi:10.24431/rw1k46d

Development and testing of mechanistic fitness-based models to predict habitat choice, behavior, and recruitment of juvenile Chinook salmon in the Arctic-Yukon-Kuskokwim region, 2015-2017

2020· dataset· en· W6907556055 on OpenAlexaboutno aff

Bibliographic record

VenueAxiom Data Science · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsForagingReplicateFlumeGraylingSalvelinusChinook windPredationJuvenile

Abstract

fetched live from OpenAlex

These data comprise the laboratory experiments on Arctic Grayling (Thymallus arcticus) and Dolly Varden charr (Salvelinus malma) as part of the larger Drift Model Project fish foraging and behavior study conducted by the Grossman Lab at the University of Georgia. Specifically, these data describe the results of many single- and multi-fish foraging experiments conducted on Arctic Grayling and Dolly Varden charr experimental specimens in an artificial stream flume in Athens, Georgia. The dataset consists of four Microsoft excel workbooks, two for single-fish experiments and two for multi-fish experiments (i.e., one workbook per species per experiment type). The data consists of: 1) individual markers for experimental specimens (or pairs in multi-fish experiments), 2) batch (i.e., experimental specimen groups), 3) predictor variable values (i.e., treatment velocities, fish sizes, days in captivity, and size rank and dominance [for multi-fish experiments]), 4) response variable values (i.e., prey capture success percentages, holding velocities, and reactive distances), and 5) other values of potential interest but not included in analyses (i.e., capture velocity, raw prey capture numbers, and variable measurements in alternate units). Fish used in all experiments were captured via hook and line between fall of 2015 and fall of 2016 from Panguingue Creek in Interior Alaska and immediately shipped to the University of Georgia upon capture. We subjected experimental specimens to a series of increasing water velocity treatment trials in an experimental stream flume to determine how prey capture success, holding velocity, and reactive distance were affected by treatment velocity, fish size, and days kept in captivity with additional categorical predictor variables of size rank (i.e., larger or smaller) and dominance (based on holding position within experimental stream flume) for multi-fish experiments. Treatment velocity and holding velocity measurements were made immediately prior to and following treatment velocity trials with a handheld electronic velocity meter. We made prey capture success measurements in real time immediately following each treatment velocity trial by recording the number of prey captured per fixed number of prey releases. Finally, reactive distance and capture velocity measurements were made after experiments had been completed via trial video analysis using the VidSync (www.vidsync.org) computer software. Dolly Varden charr and Arctic Grayling are economically and ecologically important species in Interior Alaska and understanding how these species utilize and select microhabitats has important implications for their management and overall stream fish-habitat relationship scholarship and conservation. Data are presented as two CSV files: Grayling_SingleFish_Experiment_Data.csv Dolly_SingleFish_Experiment_Data.csv

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.001
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 head0.235
GPT teacher head0.362
Teacher spread0.127 · 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 designSimulation or modeling
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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