Sea Stars 2024 Experiment - Environmental Data
Bibliographic record
Abstract
Sea star populations have undergone large-scale changes with recent marine heatwaves (MHW), thus identifying these taxa as vulnerable and sensitive to climate extremes. This project supports a developing collaboration with UVic Amanda Bates' laboratory to study impacts of climate change on understudied marine organisms. Specifically, this experiment evaluated how 1) environmental extreme events - marine heatwaves - affect the survival and physiological performance of Dermasterias imbricata (Leather sea star) and 2) whether nutritious state (access to plenty of food vs food scarcity) can mitigate, ameliorate or exacerbate the expected detrimental effects of exposure to thermal stress. This experiment targeted Leather seastar populations around Quadra Island, BC. 150 organisms were transferred to the Marna Wet Lab on Quadra Island, BC, semi-quarantined, then exposed to control temperatures (15C) and warm temperatures (>20C) for 15 days. Half of the organisms were starved, while the other half had access to food ad libitum. Survival, morphometrics, coelomocyte counts (a proxy for immune response), righting time and other physiological status metrics were evaluated throughout the experiment to evaluate responses to stressors. Transcriptomic samples were collected until additional funding can be secured for their analysis. 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 and will be available upon request until the associated manuscript has been accepted at which time the data will be made publicly available. Additional biological experimental data is held by our collaborators Dominique Maucieri and Amanda Bates (UVic). 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.020 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".