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Record W6907434899 · doi:10.21966/pvy6-nw38

iTrack Oysters February 2023 Experiment - Environmental and Oyster Health Data

2023· dataset· en· W6907434899 on OpenAlexaff

Bibliographic record

VenueHakai Institute · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMesocosmOysterCrassostreaSentinel speciesVulnerability (computing)Environmental dataEastern oysterMytilus

Abstract

fetched live from OpenAlex

This data package is a component of the Hakai Institute’s Marna Wet Lab and Caren Helbing (University of Victoria, UVic) collaborative iTrack project investigating environmental effects on eDNA and eRNA. Hakai Institute's Marna Wet Lab experimental research program uses laboratory experiments to evaluate marine organisms' responses to simulated current and future ocean environmental conditions. The overarching objective of Hakai Wet Lab experimental research is to investigate the mechanisms of vulnerability and resilience of a variety of marine species and communities under static or dynamic future environmental conditions, and understand how organisms are responding phenotypically, physiologically and/or genomically to thermal and acidification stress. This experiment was part one of a series of mesocosm experiments and took place at the Marna Wet Lab at Hakai’s Quadra Island Ecological Observatory February 22-27, 2023. The purpose of this experiment was to investigate eDNA and eRNA production and degradation under different pCO2 conditions. Adult Pacific oysters (Crassostrea gigas) were chosen as model organism and exposed to various pCO2 treatments (520 µatm, 950 µatm, and 1200 µatm) under constant temperature (10C). eDNA and eRNA samples were collected while oysters were present (Production phase) and after oysters were removed (Degradation phase). A subsample of oysters from each tank were destructively sampled for weight, size, condition/health and gill and gonad RNA at the end of the experiment. This data package contains the mesocosm temperature and carbonate chemistry data and oyster health data only and will be available upon request until the manuscript has been accepted at which time the data will be made publicly available. In light of the effort required to obtain these data and create data packages, we request all data users that, in addition to following the CC-BY license terms, they give attribution to the data providers and follow fair use guidelines: 1) respect the data providers, and provide helpful feedback on data quality, and 2) communicate and/or collaborate with Hakai Marna Wet Lab researchers and collaborators if you are considering using this dataset for manuscripts or other forms of reporting.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.970
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0440.013

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 head0.089
GPT teacher head0.336
Teacher spread0.247 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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