Poleward shifts and ecological changes of Arctic and Subarctic zooplankton and fish in response to climate variability and global climate change
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: Hein Rune Skjoldal (Norway), Carin Ashijan (USA), Louis Forter (Canada).CM 2017/E:516. Inter-annual variability of Calanus finmarchicus, C. hyperboreus and Metrida longa in subarctic waters north of Iceland 1990-2016. Astthor Gislason, Kristinn GudmundssonCM 2017/E:425. Age, growth rate, and otolith growth of polar cod (Boreogadus saida) in two fjords of Svalbard, Kongsfjorden and Rijpfjorden. Dariusz P. Fey, Jan Marcin WęsławskiCM 2017/E:529. Determining thermal preferences and limits of fish and zooplankton species using trawl survey observations. M. Elisabeth Henderson, Janet A. NyeCM 2017/E:676. Re-visiting the drivers of capelin recruitment in Newfoundland since 1991. Hannah Murphy, Pierre Pepin, Dominique RobertCM 2017/E:628. Regional differences in Ocean Conditions and Groundfish Distributional Changes in the Gulf of Alaska. Lingbo Li, Anne Hollowed, Steve Barbeaux, Edward Cokelet, Wayne Palsson, Phyllis Stabeno, Qiong YangCM 2017/E:192. Deciphering the relationship between historical abundance fluctuations in the offshore Atlantic cod (Gadus morhua) stock aroundGreenland and the environment. Karl-Michael Werner, Hans-Joachim Rätz, Ismael Núñez-Riboni, Heino O. Fock
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".