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Record W6907332401 · doi:10.21966/7nwn-bj60

Calliarthron 2023 Experiment - Environmental Data

2023· dataset· en· W6907332401 on OpenAlexaff

Bibliographic record

VenueHakai Institute · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoralline algaeOcean acidificationAlgaeMarine invertebratesInvertebrateRed algaeBiodiversityKelpCrustose

Abstract

fetched live from OpenAlex

This data package is a component of the Hakai Institute’s Marna Wet Lab and the Martone Lab (University of British Columbia, UBC) collaborative project investigating environmental effects on coralline algae. 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. Coralline algae are a diverse group of calcifying red algae that populate a wide range of marine environments globally where they provide structural support to reefs, create habitat and food resources for invertebrates and support biodiversity by promoting larval and kelp recruitment. A unique characteristic of this group of red algae is that they deposit calcium carbonate within their vegetative cell walls creating a hard thallus structure that is essential for providing support and habitat. While calcification is a key process for coralline algae physiology and ecology there is little known about the molecular, physiological and cellular mechanisms that support it and how those might be affected by changing climate conditions. Work on other tropical species of coralline algae, however, has suggested that calcifying algae might be particularly sensitive to thermal and acidification stress, though responses of temperate species are vastly understudied. Current work in Dr. Patrick Martone's lab is underway to identify putative calcification genes using tissue-specific (calcified vs. uncalcified) transcriptomes (RNAseq) in the articulated coralline alga, Calliarthron tuberculosum. The aim of this work is to understand the molecular underpinnings of the calcification mechanism by highlighting key calcification genes and developing gene-specific qPCR primers to study gene expression. Building on this foundational work, we propose to explore the interactive effects of pH and temperature on a Calvert population of Calliarthron focusing on calcification, gene expression, growth, and physiological stress responses through a multi-week mesocosm experiment. This data package includes a portion of the data from this experiment relating to mesocosm temperature and carbonate chemistry and associated protocols, processing and analysis of that data collected by the Marna Wet Lab team. Additional experimental data is held by our collaborators Emma Jourdain and Patrick Martone (UBC). All data 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)0.005
metaresearch head score (Gemma)0.008
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 score0.237
Threshold uncertainty score0.793

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.2370.049

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 head0.100
GPT teacher head0.335
Teacher spread0.235 · 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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