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Record W4256709899 · doi:10.1002/ieam.1385

Learned Discourses: Timely Scientific Opinions

2012· article· en· W4256709899 on OpenAlexaff
Peter M. Chapman

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsCLARITYGlobeSubject (documents)Library scienceConsistency (knowledge bases)Dissenting opinionPolitical sciencePublic relationsSociologyMedia studiesPsychologyLawComputer science

Abstract

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Intent. The intent of Learned Discourses is to provide a forum for open discussion. These articles reflect the professional opinions of the authors regarding scientific issues. They do not represent SETAC positions or policies. And, although they are subject to editorial review for clarity, consistency, and brevity, these articles are not peer reviewed. The Learned Discourses date from 1996 in the North America SETAC News and, when that publication was replaced by the SETAC Globe, continued there through 2005. The continued success of Learned Discourses depends on our contributors. We encourage timely submissions that will inform and stimulate discussion. We expect that many of the articles will address controversial topics, and promise to give dissenting opinions a chance to be heard. Rules. All submissions must be succinct: no longer than 1000 words, no more than 6 references, and at most one table or figure. Reference format must follow the journal requirement found on the Internet at http://www.setacjournals.org. Topics must fall within IEAM's sphere of interest. Submissions. All manuscripts should be sent via email as Word attachments to Peter M Chapman (peter_chapman@golder.com). SETAC's Learned Discourses appearing in the first 7 volumes of the SETAC Globe Newsletter (1999–2005) are available to members online at http://communities.setac.net. Members can log in with last name and SETAC member number to access the Learned Discourse Archive. Pharmaceuticals and personal care products in the environment: Cultural and spiritual perspectives, by Rai S Kookana, Bradley Moggridge, Roku Mihinui, Bruce Gray, Grant Northcott, and Alistair Boxall A recent SETAC workshop in Australia considered cultural perspectives in assessing and managing environmental impacts of these contaminants. Using assisted biotic colonization to cope with habitat loss due to sea level rise, by John Cairns Jr Replacement of lost coastal ecosystems is both an ethical imperative and in humankind's enlightened self interest. Mercury environmental quality standard for biota in Europe: Opportunities and challenges, by Davide Vignati, Stefano Polesello, Roberta Bettinetti, and Michael Bank Meaningful investigations of mercury ecotoxicology, rather than shortcuts to reduce noncompliance, are the correct way to reinforce the successful marriage of science and policy. Protection goals for aquatic plants, by Glen Thursby and Michael Lewis Three primary issues arise when dealing with protection goals for aquatic plants: what species to test; the relevance of the effects we detect; and, the level of protection required. What does the ECx tell us about the curve? Thoughts into ecological thresholds, by Marcos Krull Maybe we are not using the most appropriate and conservative metric and experimental design for the curve fitting estimate. Field surveys can support ecological risk assessment, by Yuichi Iwasaki, Takashi Kagaya, and Steve Ormerod Field surveys provide crucial evidence to evaluate the relevance of laboratory-based estimates in natural environments. Cryptic lineages—same but different?, by Alexander Feckler, Ralf Schulz, and Mirco Bundschuh The concept of cryptic lineage complexes, largely ignored, needs to be considered in ecotoxicology, field assessments, and risk assessments.

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)0.137
metaresearch head score (Gemma)0.444
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.725

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1370.444
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0090.007
Science and technology studies0.0100.018
Scholarly communication0.0720.046
Open science0.0070.032
Research integrity0.0310.034
Insufficient payload (model declined to judge)0.1230.156

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 head0.017
GPT teacher head0.301
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2012
Admission routes1
Has abstractyes

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