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Record W3037762778 · doi:10.31274/etd-20200624-120

Looking for a bottleneck: Assessment of factors influencing post-stocking survival of advanced fingerling Walleye Sander vitreus

2020· dissertation· en· W3037762778 on OpenAlexaboutno aff
Emily E. Grausgruber

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSanderStockingFisheryFishingGeographyBiologyEngineering

Abstract

fetched live from OpenAlex

Walleye Sander vitreus is a highly valued sportfish in North America. In 2001, 3.8 million anglers spent approximately 51.9 million days angling for Walleye (USFWS-USCB 2002). The popularity of Walleye has resulted in situations where demand exceeds supply, which has led to the development and implementation of stocking programs across the United States and Canada to enhance fishing opportunities (Lathrop et al. 2002), rebuild depleted stocks (Johnson et al. 1996; Li et al. 1996), and mitigate poor year classes from variable natural recruitment (Mitzner 1992; Johnson et al. 1996; Jennings et al. 2005; Logsdon et al. 2016; Reed and Staples 2017). However, mortality rates of stocked fishes can vary widely (27-95%; Stein et al. 1981; Buckmeier et al. 2005; Freedman et al. 2012; Weber et al. 2020) and small changes in survival can result in large differences in year-class strength and success of stocking initiatives. Numerous biotic and abiotic factors can influence survival during early life stages of fish, such as transportation and stocking practices (Forsberg et al. 2001; Barton et al. 2003), predation (Santucci and Wahl 1993; Buckmeier et al. 2005; Thompson et al. 2016), available forage (Johnson et al. 1996; Hoxmeier et al. 2006), competition (Le Pape and Bonhommeau 2015; Chase et al. 2016), fish origin (Kellison et al. 2000; Jonsson and Jonsson 2003; Pollock et al. 2007), body size (Litvak and Leggett 1992; Meekan et al. 2006; Grausgruber and Weber in press), and water temperatures (Akimova et al. 2016; Wagner et al. 2017). Furthermore, the aforementioned factors do not act independently of each other, making it challenging to determine their importance. The growth-predation hypothesis predicts that selective mortality should decline as individuals grow and increase in size (Anderson 1988). Increases in size are also associated with decreased predation risk (Post and Evans 1989; Miranda and Hubbard 1994), where larger body size can reduce the chances of predation due to improved maneuverability and swimming speed (Videler 1993). The argument of "bigger-is-better" (Butler 1988; Miller et al. 1988; Litvak and Leggett 1992) has led hatcheries to raise progressively larger fingerling Walleye (Halverson 2008). However, hatchery production is an expensive and labor-intensive process, where production costs are generally positively related to rearing duration and fish size (Wedemeyer 2001). Therefore, it is advantageous to evaluate factors hypothesized to limit post-stocking Walleye survival (e.g., effects of transport duration and handling practices as well as post-stocking predation and starvation) to assess whether rearing

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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