Bibliographic record
Abstract
a abalone diseases Abalone Virus Ganglioneuritis (AVG) 147 tubercle mycosis disease 510 withering syndrome 304 Acanthaster planci (coral-eating sea star) 275 Acanthochromis polyacanthus (reef damselfish) 73f Acanthocybium solandri (wahoo) 341 Acanthopagrus butcheri (Black bream) 618t Acanthuridae (surgeonfish) 340, 357 Acanthurus polyacanthus (spiny damselfish) 67 Acartia clausi 860 Acartia hudsonica 856, 857 Acartia spp.865 Acartia tonsa 856-60, 865 Acartia tsuensis 859 accelerated decades 25 Acetabularia 899 Achi Biodiversity Targets of the Convention on Biological Diversity 63 Achoerodus gouldii (Western blue groper) 618t Acropora cervicornis 647 Acropora palmata 648 adaptation in fisheries and aquaculture 55-9, 56t in aquaculture 58-9 extreme events 59 governance and management of 58 objectives 56-7 scope for 57-9 social and economic 57 adaptations, human 928, 930-2, 935 adaptive capacities 928, 930-2 Aequipecten sp.468 Aeromonas salmonicida 305 aerosols anthropogenic 9 Earth's 1, 3-4 Aetobatus flagellum (longheaded eagle ray) 124, 887 Agonidae sp.708t Alaska, volcanism in 8 Alaska Coastal Current 161 Alaska Current 161, 420 Alaskan Gyre 160 Alaskan Stream 161 albedo 1, 3, 3f changes in 8-9 effect of melting ice 28 Alexandrium catenella 306, 307,314 Alexandrium tamiyavanichii 123 Algoa Bay 509 Allocyttus niger (black oreo) 94 Alloteuthis 390 alpine glaciers 12 Amazonian rainforests, drying of 37-8 Amblyraja hyperborea (Arctic skate) 708t American shad 434t Amphibolis 731, 732 Amphiprion percula 67 Amundsen Seas 35 Amusium balloti (Southern saucer scallop) 618t Anguilla australis (shortfin eel) 97 Anguilla dieffenbachii (longfin eel) 97 Anguilla japonica (Japanese eel) 125 Anisarchus medius (Stout eelblenny) 709t Index Page references followed by 'f ' refer to Figures; those followed by 't' refer to Tables Antarctic climate change in 668-9 ice sheets 11, 37 sea-ice 11, 35 Antarctic Circumpolar Current (ACC) 663 Antarctic Convergence 663, 666 Antarctic marine ecosystem 663-4 Antarctic Oscillation (AAO) 97, 283, 483 Antarctic shag 686 Antarctic Treaty System 666 Antarctomysis maxima 679 anthropogenic aerosols 9 Aotearoa Fisheries Limited (AFL) 103 Apex Predator Ecosystem Model Estimation (APECOSM-E) model 349, 556, 786 Aptenodytes patagonicus (king penguin) 664 aquaculture climate change and the risk to aquaculture 508 growth, expansion and intensification 47-8 impact of climate change on 53-4 industries 750t interactions between capture fisheries and 54-5 natural and human drivers of 48-9 production 45, 50 regional differences and aggregated impacts 50-3 temperature and ocean chemistry 508-60 Arabian Gulf tropical marine fishes case study 888 aragonite 14-16 aragonite saturation state 64 Brazilian coast 461 Caribbean Sea 647-8 Canadian fisheries 441, 442 Pacific Island region 352, 355f, 359, 368 United States 171, 175, 176, 184, 191-2 Arctic annual mean surface air temperature 1960-2009 27f methane levels 5 warming 3 Arctic Current 424 Arctic marine ecosystems, climate change impacts on 703-23 commercial fish density 716-18 ocean conditions 711-16 Arctic Ocean heat absorption per square meter 3 temperature of 1 Arctic Oscillation 122 Arctic sea ice decline in 35 melting of, effect on local albedo 3 September 11f summer 9, 11 summer to 2100 under IPCC RCP4.5.35 Arctocephalus forsteri (fur seal) 94 Arctocephalus gazella (Antarctic fur seal) 664 Arctocephalus tropicalis (sub-Antarctic fur seals) 664 Arctogadus glacialis (Arctic cod; ice cod) 706, 707, 708t, 709t Argo Programs 31 Argobuccinum spp.(pustular triton) 257t Argopecten irradians 751 Argopecten purpuratus (bay scallop) 257t, 268-70, 294,295t, 296t Argyrosomus japonicus 507t Argyrosomus thorpei 492 Arica annual sea level in 252f oxygen minimum layer 253f Arnoglossus laterna (scaldfish) 393 Arripis georgianus (Australian herring) 618t arrowtooth flounder 434t Artedidraconidae (plunderfishes) 663 Artediellus uncinatus (Arctic hookear sculpin) 708t Artemia nauplii 828 Artificial Neural Networks (ANNs) 282, 283-4, 285 Artisanal Fishing Register (RPA) 240 Atheresthes stomias (arrowtooth flounder) 171 Athyonidium chilensis (sea cucumber) 259t Atlantic Meridional Overturning Circulation (AMOC) 12, 32, 181, 182, 187, 706, 775 Atlantic Multi-decadal Oscillation (AMO) 64, 69, 195f, 198, 388, 424, 775 Atlantic salmon 422, 423, 432, 438 Atlantis ecosystem model for the U.S. West Coast 171 Atmospheric Circulation Index of the North Atlantic
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.716 | 0.534 |
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".