Supplementary environmental point data: "A climate-associated multi-species cryptic genetic cline in the northwest Atlantic"
Bibliographic record
Abstract
This is supplementary environmental data used in environmental population structure analyses: "A climate-associated multi-species cryptic genetic cline in the northwest Atlantic" This data is described in the methods as: Temperature and salinity data were aggregated to seasonal climatological data (averaged across 2002-2012) layers, effectively corresponding with winter (January – March), spring (April – June), summer (July – September), and fall (October – December). Seasonal sea surface temperature (SST) and sea surface salinity (SSS) were assembled at spatial resolutions interpretable to 1 km2 from Level 3 SST climatological satellite data, including Advanced Very High Resolution Radiometer data (AVHRR Atlantic; compiled by Fisheries and Oceans Canada) and global oceanographic climatological SSS composites (Tyberghein et al. 2012). Benthic temperature and benthic salinity climatological data layers were assembled at spatial resolutions interpretable to 8 km2 from a numerical climatological model (GLORYS2V1) adapted to the study area by Fisheries and Oceans Canada. We represented topographic complexity of the seafloor (interpretable to 1 km2) by east–west and north–south components of aspect, slope, plan and profile curvature, and rugosity (Sbrocco & Barber 2013). These data layers were used as predictive surfaces for each of the five species native to the range (Table 1). From these data layers, we extracted point estimates of each environmental variable for each genetic sample location to evaluate genetic-environmental relationships. This dataset represents these point estimates.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.621 | 0.150 |
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".