Bibliographic record
Abstract
In their Report “A mid-term analysis of progress toward international biodiversity targets” (10 October, p. [241][1]; published online 2 October), D. P. Tittensor and colleagues concluded that despite accelerating policy and management responses to the biodiversity crisis on a global scale, these efforts are unlikely to improve the state of biodiversity by 2020. They did not acknowledge that sustained national policies in China have already produced positive effects on biodiversity. The Chinese government initiated in 1999 the Natural Forest Resources Conservation Program and the Restoring Farmland into Forest Program. Logging has been prohibited in most natural forests, and cultivated land on areas with slopes of more than 25 degrees must be restored to forests or grasslands ([ 1 ][2]). The central government subsidized forest management and conservation, as well as seedling cultivation and reforestation ([ 2 ][3], [ 3 ][4]). Households that have returned their cultivated land to forests received subsidies from the central government ([ 2 ][3], [ 3 ][4]). Recently, the State Council updated the policies and increased subsidies to further promote ecosystem restoration ([ 2 ][3]). A number of other key ecological programs continue to be implemented, such as forest belt construction in the Yangtze River basin. More than US$80 billion has been invested in these programs ([ 1 ][2]). Ecological conditions have improved ([ 2 ][3]). Forest area, growing stock (the total stock volume of trees growing in land), and coverage rate (the percentage of area of afforested land compared with total land) all increased between 2009 and 2013 ([ 4 ][5]–[ 6 ][6]). These programs have contributed to progress toward Aichi Targets 5, 14, and 15 ([ 2 ][3]), although these policies need to be fine-tuned to best fit the local environment. 1. [↵][7] Secretariat of the Convention on Biological Diversity, Global Biodiversity Outlook 4 (Montreal, 2014). 2. [↵][8] Ministry of Environmental Protection, “China's 5th national report to the CBD” ([www.cbd.int/doc/world/cn/cn-nr-05-en.pdf][9]). 3. [↵][10] 1. P. W. Leadley 2. et al ., “Progress towards the aichi biodiversity targets: An assessment of biodiversity trends, policy scenarios, and key actions” (Secretariat of the Convention on Biological Diversity, Montreal, 2014). 4. [↵][11] State Forestry Administration, “China Forestry Statistics” (China Forestry Press, Beijing, 2013). 5. State Forestry Administration, “The promulgation of the eighth national forest resource inventory” ([www.forestry.gov.cn/][12]). 6. [↵][13] 1. H. Xu 2. et al ., BioScience 59, 843 (2009). [OpenUrl][14][Abstract/FREE Full Text][15] [1]: /lookup/doi/10.1126/science.1257484 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-6 [7]: #xref-ref-1-1 View reference 1 in text [8]: #xref-ref-2-1 View reference 2 in text [9]: http://www.cbd.int/doc/world/cn/cn-nr-05-en.pdf [10]: #xref-ref-3-1 View reference 3 in text [11]: #xref-ref-4-1 View reference 4 in text [12]: http://www.forestry.gov.cn/ [13]: #xref-ref-6-1 View reference 6 in text [14]: {openurl}?query=rft.jtitle%253DBioScience%26rft_id%253Dinfo%253Adoi%252F10.1525%252Fbio.2009.59.10.6%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [15]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImJpb3NjaWVuY2UiO3M6NToicmVzaWQiO3M6OToiNTkvMTAvODQzIjtzOjQ6ImF0b20iO3M6MjM6Ii9zY2kvMzQ2LzYyMTMvMTA2OC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".