Bibliometric Investigation of Climate Change Literature in Fisheries using Dimensions.AI Database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Climate change is the most critical and contentious issue confronting the globe today. Changes in rainfall patterns and temperature have already influenced the fisheries sector unfavourably. This bibliometric analysis examined the publications on climate change’s effects on fisheries from 2008 to 2022, using Dimension-listed journals with DOIs. Keywords, authors, co-citations and journal trends are studied. Methods: A total of 180 research articles were analysed using Dimension (https://dimension.ai) with search terms’ climate change,’ ‘fishery’, ‘fisheries’ and ‘aquaculture’. The dataset was updated on May 20, 2022. A bibliometric map was created using the R Biblioshiny package. Result: The number of articles discussing climate change and its influence on fisheries has risen dramatically. Several journals cover this topic, the most prominent of which is Fisheries Oceanography. Animals, fisheries, climate change, ecosystems and fishes are among the most often used keywords. Cheung WWL is the most prolific author and has published the most publications over the 15-year study period. Among countries, Canada has the most popular articles and China has the most authors. This research summarises the most popular authors, publications and keywords used in papers on climate change subjects. Furthermore, their impact on fisheries gives information to researchers interested in climate change research and its impact on fisheries. Finally, ample scope exists for developing adaptation strategies through insightful research and funding.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.086 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it