This is an old revision of the document!
This is a place where we can store publications relevant to our work, that might be featured in proposals or deliverables or that we simply deem useful. And hopefully it will be easier to search for them here then on a diffuse number of local hard disks…
Here is a quick and dirty hack to access non-open-access articles in case they are included in the journals that the UPC has subscriptions for and supposing that you have UPC credentials. This method is particularly useful for DOI searches (That the UPC Library tries to keep a secret it seemed to me…):
UPC Library DOI search portal (https://doi-org.recursos.biblioteca.upc.edu/)
Then grab the tail of your DOI html address like “10.1111/gcb.14917” from
https://doi.org/10.1111/gcb.14917
or directly from (restrictive) publisher site like this one
https://onlinelibrary.wiley.com/doi/10.1111/gcb.14917
and paste it in the DOI search field and hit enter. If you are lucky your publication may be found and accessed that way as in this example….
https://doi-org.recursos.biblioteca.upc.edu/10.1111/gcb.14917
Author | Title, Description, DOI, Document |
---|---|
A. Krause | Legacy Effects from Historical Environmental Changes Dominate Future Terrestrial Carbon Uptake |
2020 | |
https://doi.org/10.1029/2020EF001674 | |
krause-legacy_effects_from_historical_environmental_changes_2020ef001674.pdf | |
Philip Vergragt et al | Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates |
2011 | This paper investigates if and how carbon capture and storage (CCS) could help to avoid reinforcing fossil fuel lock-in. The outcome is that a large-scale BECCS development could be feasible under certain conditions, thus largely avoiding the risk of reinforced fossil fuel lock-in. Keywords: Carbon capture and storage, Biomass, Fossil fuel |
https://doi-org.recursos.biblioteca.upc.edu/10.1111/gcb.15117 | |
1-s2.0-s0959378011000215-main.pdf | |
Hui Yang | Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates |
2020 | Uses the GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs).Model estimates are 365 ± 66 Pg C compared to 275 (±13.5%) Pg C from GlobBiomass. The results suggest that TRENDY v6 DGVMs tend to underestimate biomass loss from anthropogenic disturbances. |
https://doi-org.recursos.biblioteca.upc.edu/10.1111/gcb.15117 | |
globchangebiol_-_2020_-_yang_-_comparison_of_forest_above_ground_biomass.pdf |