TRACKING LONG‐TERM CHANGES IN CLIMATE USING ALGAL INDICATORS IN LAKE SEDIMENTS
Notice bibliographique
Résumé
Interest in climate change research has taken on new relevance with the realization that human activities, such as the accelerated release of the so‐called greenhouse gases, may be altering the thermal properties of our atmosphere. Important social, economic, and scientific questions include the following. Is climate changing? If so, can these changes be related to human activities? Are episodes of extreme weather, such as droughts or hurricanes, increasing in frequency? Long‐term meteorological data, on broad spatial and temporal scales, are needed to answer these questions. Unfortunately, such data were never gathered; therefore, indirect proxy methods must be used to infer past climatic trends. A relatively untapped source of paleoclimate data is based on hindcasting past climatic trends using the environmental optima and tolerances of algae (especially diatoms) preserved in lake sediment profiles. Paleophycologists have used two approaches. Although still controversial, attempts have been made to directly infer climatic variables, such as temperature, from past algal assemblages. The main assumption with these types of analyses is that species composition is either directly related to temperature or that algal assemblages are related to some variable linearly related to temperature. The second more commonly used approach is to infer a limnological variable (e.g. water chemistry, lake ice cover, etc.) that is related to climate. Although paleolimnological approaches are broadly similar across climatic regions, the environmental gradients that paleophycologists track can be very different. For example, climatic inferences in polar regions have focused on past lake ice conditions, whereas in lakes near arctic treeline ecotones, paleophycologists have developed methods to infer past lakewater‐dissolved organic carbon, because this variable has been linked to the density of coniferous trees in a drainage basin. In closed‐basin lakes in arid and semiarid regions, past lakewater salinity, which can be robustly reconstructed from fossil algal assemblages, is closely tied to the balance of evaporation and precipitation (i.e. drought frequency). Some recent examples of paleophycolgical work include the documentation of striking environmental changes in high arctic environments in the 19th century believed to be related to climate warming. Meanwhile, diatom‐based reconstructions of salinity (e.g. the Great Plains of North America and Africa) have revealed prolonged periods of droughts over the last few millennia that have greatly exceeded those recorded during recent times. Marked climatic variability that is outside the range captured by the instrumental record has a strong bearing on sustainability of human societies. Only with a long‐term perspective can we understand natural climatic variability and the potential influences of human activities on climate and thereby increase our ability to understand future climate.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».