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Record W6990315885

Detecting wild fish and zooplankton near fish
\nfarms during and after fallow periods in Southern
\nNewfoundland

2019· dissertation· en· W6990315885 on OpenAlexaboutno aff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsBayZooplanktonDiel vertical migrationAbundance (ecology)Acoustic Doppler current profilerFish <Actinopterygii>Water columnCoastal fishForage fishDemersal fish
DOInot available

Abstract

fetched live from OpenAlex

From November 2016 until September 2017, acoustic Doppler current profilers \nwere deployed in two neighboring bays in Southern Newfoundland, East Bay and \nCinq Island Bay. We set out to determine the in \nuences that aquaculture had on \nwild fish abundance in the ecosystem during fallow periods. Fallow periods describe \nthe times when farms were not stocked with fish. It was hypothesized that during a \ntime where a fish farm was newly inactive there would be a different abundance of \nwild fish nearby than during a time when the farm was active. In our study, both \nEast Bay and Cinq Island Bay were fallow prior to November until June/July of the \nfollowing year. After that time, fish farms were restocked with Atlantic Salmon. The \nacoustic Doppler current profilers were configured to collect data without the typical \naveraging of acoustic pings into ensemble averages. This processing allowed for the in- \nstruments to act as fish detecting sonars. We calculated volume backscatter strength, \nfish counts, fish depths and target strengths of detected fish. Fish schools and diur- \nnal migration patterns occurred frequently and on some occasions, high backscatter \nintensities persisted for several hours. Depths of fish appeared to be similar during \nNovember, December, January, March and April. Summer months of June, July, Au- \ngust and September had opposing depth distributions. In Summer months, there were \nfish primarily at shallow depths and in Winter months fish were primarily at deeper \ndepths. Summarizing the entirety of the time series resulted in depths distributions \nthroughout the entire water column in East Bay suggesting that diel vertical migrat- \ning species were commonly present. In contrast to this, Cinq Island Bay showed fish \nprimarily at 50 meters, indicating that a different fish species frequented this area. Similar results from target strength distributions suggested two species or behaviors \npresent in East Bay and one species present in Cinq Island Bay. Shifts of fish types \nor behaviors in East Bay occurred during the same time periods as the beginning \nof fish farm activity and also the start of the Summer season. Both bays displayed \nconcurrent increased fish counts in January and May with a larger wild fish abun- \ndance in East Bay than in Cinq Island Bay. The amount of fish in both bays differed \nduring and after fallow periods which coincided with seasonal changes. Because of \nthis timing, distinguishing the effects of fallow periods from seasonal changes was not \npossible. This study demonstrates the value of using acoustic monitoring methods \nfor collecting data from aquaculture sites. Altering future studies to include longer \ntime series, simultaneous fish sampling and multiple acoustic Doppler current profil- \ners in each bay could provide additional important data that may lead to concrete \nconclusions about aquaculture in \nuences on wild fish species.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.648
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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