MétaCan
Menu
Back to cohort
Record W4381986065 · doi:10.1111/jfr3.12914

The growing strength of the ‘Journal of Flood Risk Management’ community

2023· article· en· W4381986065 on OpenAlexaboutno aff
Chrissy Mitchell

Bibliographic record

VenueJournal of Flood Risk Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythChinaVulnerability (computing)Flood risk managementGeographyDiversity (politics)Risk managementPolitical scienceEnvironmental planningEnvironmental resource managementBusinessEnvironmental scienceLawComputer science

Abstract

fetched live from OpenAlex

The Journal of Flood Risk Management provides an international platform for knowledge sharing in all areas related to flood risk. Not even halfway through 2023, the Journal has already received papers from over 36 different countries, outlining the diversity of coverage. United Kingdom, China, United States and Iran have submitted the majority. Closely followed by Germany, Italy, Egypt, Netherlands, Canada, the Republic of Korea and India. Perhaps more important to authors is the readership, which shows a healthy global spread, and in 2022, 350,000 full-text article views were undertaken. Those countries mentioned above lead the way in accessing the articles, alongside the Philippines, Australia and Malaysia. The top 10 most viewed articles all having well over 2000 views each. They show no clear trend in the topic area, covering the many different aspects of flood risk management. In this issue, it is excellent to see the diversity in topics. A number of articles consider the decision-making aspects of flood management. Chyon et al., provide an integrated assessment of flood risk, where both the physical hazard and the socio-economic exposure alongside vulnerability are considered, providing an approach that can successfully improve adaptive capacity and can be applied elsewhere (Chyon et al., 2022). Davids et al. (2023), investigates how homeowners “rationalities” respond to expert advice, using Cultural Theory to invite behavioural skills beyond engineering into consideration. Interestingly, Tyler et al. (2023) review funding scenarios for a number of coastal counties in south-eastern United States, where results show counties that are socially vulnerable are less likely to receive funding. A number of articles focus on urban aspects. Chang et al. (2022) consider the lack of operational or quantitative stormwater management resilience indicators that can support a reduction in inundation for short-duration flooding. Alongside the same theme of looking at further evidence for measures, Azhar et al. (2023) consider the safety criteria for flooding in relation to stationary vehicles. Highlighting the shift in stability being highly dependent on the road conditions. Hosseinzadeh et al. (2023) consider potential detention pond placement and support for decision makers when planning design requirements. When considering the improvements of predictions, Moon et al. (2023) use nomographs to consider future flooding of an urban river. Sahraei et al., 2022 use a GIS-based multi-criteria decision-making approach for large ungauged watersheds, focusing on susceptibility when having little access to data: an interesting hybrid method that seemingly outperforms some previous methods when compared with known historic maps. Mahmoodi et al. (2023) compare different weighting methods for watershed multi-criteria models and highlight the importance of prioritizing a number of factors that go into the decision-making. While Hu et al. (2023) look to improve the estimation of flood frequency statistics, they suggest it is an approach that can be further used in other ungauged catchments. Finally, in this issue, Collins et al. (2023) consider natural flood management and strongly conclude that these sorts of natural interventions in large permeable catchments should be considered further. Sharpe et al. (2023) focus on the impact of riparian forests on hydraulic roughness, looking at the reliability of roughness coefficients. Ceccato and Simonini (2023) consider levee failure mechanisms, in particular focusing on small cavities, in relation to the Panaro levee breach in 2020. In the past 15 years (2009–2022), the average number of papers submitted to the Journal of Flood Risk Management has been 165 per year. It is recognised that this is often the accumulation of considerable time, study and financial commitment by the authors, which makes it disappointing when not all of these papers make it through to final publication. Guidance is provided to authors prior to submission (Wiley, 2023a). Some of the more common reasons for not being published include no clear novelty or advance in scientific knowledge presented, written to a lower standard, too much of an overlap with a similar authored paper published elsewhere, no response by an author following feedback, and a request for minor or major changes. Perhaps most frustrating is a paper that has really good scientific advances, but where the language used is challenging to follow. For this, a service is offered to authors (Wiley, 2023a) or we suggest a native reader undertakes a thorough check before submission. When an author submits an article, the peer review process includes a thorough review and response from an associate editor and two reviewers, before consideration by the editor in chief. If changes are recommended then the revised article can often require a second or even third iteration of review, repeating this same process. This journal, as do many others, relies on the peer review of experts in the field to undertake these reviews and is very grateful for those who volunteer their time and expertise to do so. The time it takes to respond to an author and ultimately publish is directly dependent on the time it takes to find reviewers willing to undertake a review, as well as the time it takes reviewers to respond. Although this robust process has previously occurred in less than 1 month, it can take significantly longer. The journal openly welcomes new potential reviewers (Wiley, 2023b) and kindly requests that if an invitation to review is sent to you, that a decision (either way) and a suggestion of who might be appropriate to ask and available further to yourself, is gratefully received.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 teacher head, not a consensus.

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Flood Risk ManagementSame topicFlood Risk Assessment and ManagementFrench-language works237,207