Modelled spatial predictions of the distribution and density of Antarctic krill in the South Scotia Sea between 2011-2020
Bibliographic record
Abstract
This dataset contains gridded spatial predictions of the distribution and density of Antarctic krill (Euphausia superba) in the South Scotia Sea, specifically within Subarea 48.2 of the Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR). Both year-specific and decadal mean predictions are provided across years 2011-2020. All predictions were generated from a two-part hurdle model which used input data from (i) a spatially and temporally consistent acoustic krill survey around the South Orkney Islands and (ii) year-specific environmental covariates. The first hurdle model component was a binomial Generalized Additive Model (GAM) fitted to binary presence-absence krill data which predicts the probability of krill presence. The second component was a Gaussian GAM fitted to non-zero krill data which predicts krill density. Finally, these components were combined to identify where krill were both likely to be present and occur at high densities. Full model details are given in the associated publication. This dataset provides the spatial predictions generated from the binomial GAM, Gaussian GAM, and their combined product. Funding: PNT, SF and JJF were supported by the British Antarctic Survey's National Capability Antarctic Logistics and Infrastructure programme CONSEC, supported by the Natural Environment Research Council, a part of UK Research and Innovation.; VW-E and JJF were supported by the Pew Charitable Trusts under grant PA00034295. The South Orkney Islands acoustic trawl survey is part of the ongoing Norwegian Institute of Marine Research (IMR) project KRILL (p.no. 14246), which is supported by the Norwegian Research Council (NFR grant 222798), the Norwegian Ministry of Foreign Affairs, and IMR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".