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Record W6963919246 · doi:10.24431/rw1k46c

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· W6963919246 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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_Dominance_Experiment_Data.csv Dolly_Dominance_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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0050.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.235
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

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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