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Record W2910339111 · doi:10.1002/lno.11112

Editorial: Long‐term studies in limnology and oceanography

2019· editorial· en· W2910339111 on OpenAlexaboutno aff
Marguerite A. Xenopoulos

Bibliographic record

VenueLimnology and Oceanography · 2019
Typeeditorial
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsLimnologyWetlandAquatic ecosystemAnalyticsEcologyTerm (time)Environmental scienceGeographyEnvironmental resource managementData scienceBiologyComputer science

Abstract

fetched live from OpenAlex

We use a variety of approaches and techniques to study aquatic ecosystems. Among the most powerful of these are studies that involve repeated measurements of the same variables over longer periods (i.e., many years to decades). The reason for this is that changes in our streams, rivers, lakes, wetlands, estuaries, and oceans may take place and are only observable over prolonged periods. Consequently, we need systematic and sustained environmental monitoring to document the effects of global change on aquatic ecosystems and understand its causes and consequences. The case that long-term research is instrumental in advancing scientific knowledge and policy was made in two recent publications entitled, “Long-term studies contribute disproportionately to ecology and policy” (Hughes et al. 2017. Bioscience) and “Long-term research in ecology and evolution: a survey of challenges and opportunities” (Kuebbing et al. 2018. Ecological Monographs). These papers found that long-term studies are cited more, are more likely to be published in highly regarded scientific journals, and play a larger role in shaping environmental policy. Despite this, long-term studies constitute a small fraction of aquatic science research. From my quick survey, only 5–6% of publications on lakes, oceans, estuaries, and rivers include long-term data collections (data from Clarivate Analytics Web of Science for papers published between 1981 and 2018). Is long-term monitoring vital for aquatic sciences and should we be doing more of these studies? I think so. There are many examples where continuous time-series documented ecosystem change and allowed for stakeholders and decision makers to work together to reduce or fix the problem. Acid rain provides perhaps one of the most famous examples of the value of long-term research. A series of measurements in northeast U.S.A., eastern Canada, and Europe demonstrated declining pH in rain, snow, and water that resulted in measurable impacts on lake ecosystems. Another example of the value of long-term data collection are the continuing observations of atmospheric levels of carbon dioxide (CO2) at the Mauna Loa Observatory in Hawaii which eventually provided clues about the changing global climate. Coupled to the atmospheric CO2 records are 30 years of rising seawater partial pressure CO2 measurements at nearby station ALOHA, which have been accompanied by simultaneous increases in ocean acidity. It can take many years to see the benefits of long-term data collection. Unfortunately, long-term studies are chronically underfunded in many countries and can be difficult to sustain through multiple grant cycles. Granting agencies generally promote research that provides immediate tangible products and benefits and typically fund studies for limited durations (1–5 years). Compounding this funding problem is the issue that many of us do a poor job of explaining the necessity and importance of long-term research to the public. Even among our peers, the value of long-term research can be a tough sell. I recently asked two of my Canadian colleagues to review one of my grant applications, which included the prospect of long-term research. Although one of my colleagues encouraged me to increase the long-term monitoring facet of my proposal, the other one was more critical. It was suggested to me to de-emphasize this aspect of my proposal because of the perception that the review panel would not consider it novel and would hesitate to allocate public research dollars to long-term monitoring research. Altogether, we must do better to promote long-term studies and the many benefits that they provide. Not only can a long-term time-series generate unique knowledge, but it can also lead to the discovery of surprise findings. We should support our peers through their long-term research endeavors and remind our politicians and funding agencies that long-term studies are essential to the creation of effective environmental policy. This past year, we at Limnology and Oceanography celebrated the anniversaries of two long-term monitoring research sites with the creation of two virtual issues; the 30th Anniversary of the Hawaii Ocean Time-series (https://aslopubs.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1939-5590.ALOHA30) and the 50th Anniversary of the Experimental Lakes Area (https://aslopubs.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1939-5590.ELA50?campaign=dartwol|4795815320). We are finishing the year with this capstone special issue on long-term perspectives in aquatic research. But let us not end there. I hope to see more long-term studies published in Limnology and Oceanography.

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)0.010
metaresearch head score (Gemma)0.052
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.052
Meta-epidemiology (narrow)0.0070.002
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0070.003
Science and technology studies0.0050.005
Scholarly communication0.0110.009
Open science0.0060.003
Research integrity0.0190.027
Insufficient payload (model declined to judge)0.0260.023

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 head0.011
GPT teacher head0.240
Teacher spread0.228 · 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
GenreEditorial

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

Citations8
Published2019
Admission routes1
Has abstractyes

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