Women in limnology: From a historical perspective to a present‐day evaluation
Bibliographic record
Abstract
Abstract Research in limnology is nurtured by the work of many fascinating and passionate women, who have contributed enormously to our understanding of inland waters. Female limnologists have promoted and established the bases of our knowledge about inland waters and fostered the need of protecting the values of those ecosystems. However, on numerous occasions, their contribution to the advancement of limnology has not been duly recognized. Here, we review the presence of women in limnology through the history of the discipline: from the pioneers who contributed to the origins to present day' developments. We aim at visibilizing those scientists and establish them as role models. We also analyze in a simple and illustrative way the current situation of women in limnology, the scientific barriers they must deal with, and their future prospects. Multiple aspects fostering the visibility of a scientist, such as their presence in conferences, awards, or representation in societal or editorial boards show a significant gap, with none of those aspects showing a similar visibility of women and men in limnology. This article raises awareness of the obstacles that women in limnology faced and still face, and encourages to embrace models of leadership, scientific management, and assessment of research performance far from those commonly established. This article is categorized under: Science of Water > Methods Water and Life > Methods
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".