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Enregistrement W2910339111 · doi:10.1002/lno.11112

Editorial: Long‐term studies in limnology and oceanography

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

Notice bibliographique

RevueLimnology and Oceanography · 2019
Typeeditorial
Langueen
DomaineEarth and Planetary Sciences
ThématiqueMarine and coastal ecosystems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLimnologyWetlandAquatic ecosystemAnalyticsEcologyTerm (time)Environmental scienceGeographyEnvironmental resource managementData scienceBiologyComputer science

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,052
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,052
Méta-épidémiologie (sens strict)0,0070,002
Méta-épidémiologie (sens large)0,0060,005
Bibliométrie0,0070,003
Études des sciences et des technologies0,0050,005
Communication savante0,0110,009
Science ouverte0,0060,003
Intégrité de la recherche0,0190,027
Charge utile insuffisante (le modèle a refusé de juger)0,0260,023

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,240
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2019
Routes d'admission1
Résumé présentoui

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