The co-evolution of technological promises, modelling, policies and climate change targets

The nature and framing of climate targets in international politics has changed substantially since their early expressions in the 1980s. Here, we describe their evolution in five phases-from 'climate stabilization' to specific 'temperature outcomes'-co-evolving with wider climate politics and policy, modelling methods and scenarios, and technological promises (from nuclear power to carbon removal). We argue that this co-evolution has enabled policy prevarication, leaving mitigation poorly delivered, yet the technological promises often remain buried in the models used to inform policy. We conclude with a call to recognise and break this pattern to unleash more effective and just climate policy. This Perspective maps the history of climate targets and shows how the international goal of avoiding dangerous climate change has been reinterpreted in the light of new modelling methods and technological promises, ultimately enabling policy prevarication and limiting mitigation

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Somatic symptom overlap in Beck Depression Inventory-II scores following myocardial infarction

Background Depression measures that include somatic symptoms may inflate severity estimates among medically ill patients, including those with cardiovascular disease. Aims To evaluate whether people receiving in-patient treatment following acute myocardial infarction (AMI) had higher somatic symptom scores on the Beck Depression Inventory-II (BDI-II) than a non-medically ill control group matched on cognitive/affective scores. Method Somatic scores on the BDI-II were compared between 209 patients admitted to hospital following an AMI and 209 psychiatry out-patients matched on gender, age and cognitive/affective scores, and between 366 post-AMI patients and 366 undergraduate students matched on gender and cognitive/affective scores. Results Somatic symptoms accounted for 44.1% of total BDI-II score for the 209 post-AMI and psychiatry out-patient groups, 52.7% for the 366 post-AMI patients and 46.4% for the students. Post-AMI patients had somatic scores on average 1.1 points higher than the students (P < 0.001). Across groups, somatic scores accounted for approximately 70% of low total scores (BDI-II < 4) v. approximately 35% in patients with total BDI-II scores of 12 or more. Conclusions Our findings contradict assertions that self-report depressive symptom measures inflate severity scores in post-AMI patients. However, the preponderance of somatic symptoms at low score levels across groups suggests that BDI-II scores may include a small amount of somatic symptom variance not necessarily related to depression in post-AMI and non-medically ill respondents