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Southern Ocean Not So Pristine

2008· letter· en· W2024536443 on OpenAlexaff
Louise K. Blight, David G. Ainley

Bibliographic record

VenueScience · 2008
Typeletter
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFishingMarine ecosystemOceanographyClimate changePelagic zoneExtinction (optical mineralogy)Demersal zoneMarine lifeGeographyFisheryMegafaunaEcosystemEnvironmental scienceGeologyEcologyPleistoceneBiologyPaleontology

Abstract

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The Report “A global map of human impact on marine ecosystems” (B. S. Halpern et al. , 15 February, p. [948][1]) provides a timely overview of anthropogenic effects on even the farthest reaches of Earth's oceans. However, we contend that, for at least one region, using data from only the past decade leads to misleading results. A widespread perception exists that waters south of the Antarctic Polar Frontal Zone—i.e., the Southern Ocean (SO)—are still nearly pristine ([1][2], [2][3]). In fact, the northern portion of the SO saw virtually all cetacean populations removed long ago ([3][4]), and in subsequent years (1960s to 1980s) the largest stocks of demersal fish in the Indian Ocean and Scotia Sea/Atlantic Ocean sectors were also fished to commercial extinction ([4][5], [5][6]). Historically exploited fish species and cetaceans show little signs of recovery in the SO, and recent legal commercial fishing activity has been correspondingly low ([6][7]). It is thus no surprise that the modeling used by Halpern et al . shows little anthropogenic impact in these sectors apart from that of climate change. The authors acknowledge that accounting for current illegal, unregulated, and unreported fishing in these waters might show increased human impacts. The additional consideration of historical data should cause Halpern et al . to temper their conclusion that for the world's oceans “large areas of relatively little human impact remain, particularly near the poles.” 1. 1.[↵][8]1. J. P. Croxall, 2. P. N. Trathan, 3. E. J. Murphy , Science 297, 1510 (2002). [OpenUrl][9][Abstract/FREE Full Text][10] 2. 2.[↵][11]1. V. Smetacek, 2. S. Nicol , Nature 437, 367 (2005). [OpenUrl][12] 3. 3.[↵][13]1. J.A. Estes, 2. D.P. Demaster, 3. D.F. Doak, 4. T.E. Williams, 5. R. L. Brownell Jr. 1. L. Ballance 2. et al. , in Whales, Whaling and Ocean Ecosystems, J.A. Estes, D.P. Demaster, D.F. Doak, T.E. Williams, R. L. Brownell Jr., Eds. (Univ. of California Press, Berkeley, CA, 2006), pp. 215-230. 4. 4.[↵][14]1. O. Gon, 2. P. C. Heemstra , Fishes of the Southern Ocean (J. L. B. Smith Institute of Ichthyology, Grahamstown, South Africa, 1990). 5. 5.[↵][15]1. K.-H. Kock , Antarctic Fish and Fisheries (Cambridge Univ. Press, Cambridge, 1992). 6. 6.[↵][16]Fishery Reports, Convention on the Conservation of Antarctic Marine Living Resources ([www.ccamlr.org/pu/e/e_pubs/fr/drt.htm][17]). [1]: /lookup/doi/10.1126/science.1149345 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #ref-6 [8]: #xref-ref-1-1 View reference 1. in text [9]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DCroxall%26rft.auinit1%253DJ.%2BP.%26rft.volume%253D297%26rft.issue%253D5586%26rft.spage%253D1510%26rft.epage%253D1514%26rft.atitle%253DEnvironmental%2BChange%2Band%2BAntarctic%2BSeabird%2BPopulations%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.1071987%26rft_id%253Dinfo%253Apmid%252F12202819%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [10]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIyOTcvNTU4Ni8xNTEwIjtzOjQ6ImF0b20iO3M6MjU6Ii9zY2kvMzIxLzU4OTUvMTQ0My4yLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [11]: #xref-ref-2-1 View reference 2. in text [12]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D437%26rft.spage%253D367%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: #xref-ref-3-1 View reference 3. in text [14]: #xref-ref-4-1 View reference 4. in text [15]: #xref-ref-5-1 View reference 5. in text [16]: #xref-ref-6-1 View reference 6. in text [17]: http://www.ccamlr.org/pu/e/e_pubs/fr/drt.htm

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0330.006

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.

Opus teacher head0.011
GPT teacher head0.218
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

Quick stats

Citations9
Published2008
Admission routes1
Has abstractyes

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