Bibliographic record
Abstract
Tussen glastuinbouwsector en overheid is een doel voor de totale CO2-emissie in 2020 van 4,6 Mton overeengekomen.In 2017 is de CO2-emissie toegenomen naar 5,9 Mton.De glastuinbouw zat hiermee boven het doel.In de periode 2010-2014 daalde de CO2-emissie substantieel.Dit kwam vooral door krimp van het areaal, minder verkoop van elektriciteit en vermindering van het energiegebruik per m 2 kas.In de periode 2014-2017 trad een lichte stijging op.Na temperatuurcorrectie was de CO2-emissie min of meer stabiel.In deze periode waren vooral krimp van het areaal, meer duurzame energie, meer verkoop elektriciteit en toename van het energiegebruik per m 2 van invloed.Bij het energiegebruik per m 2 was in de periode 2014-2017 zowel intensivering van de elektriciteitsvraag door belichting als van de warmtevraag van invloed.Het aandeel duurzame energie in het totaal energiegebruik groeide in 2017 naar 6,5%.Deze groei zat bij inkoop duurzame elektriciteit en aardwarmte.De energie-efficiëntie bleef in 2017 gelijk.The greenhouse horticulture sector and government have agreed on a target of 4.6 megatonnes in 2020.In 2017, CO2 emissions increased to 5.9 megatonnes, which means that the greenhouse horticulture sector is above the goal.CO2 emissions fell considerably in the 2010-2014 period, primarily due to a reduction in acreage, reduced electricity sales and lower energy consumption per m 2 of greenhouse.There was a slight increase between 2014 and 2017, but after correction for temperature, CO2 emissions were broadly stable.Influencing factors in this period were a reduction in acreage, more sustainable energy, increased electricity sales and an increase in energy consumption per m 2 .For energy consumption per m 2 , a strengthening of demand for electricity from lighting and for heat was an influencing factor in the 2014-2017 period.In 2017, the proportion of sustainable energy in the total energy consumption increased to 6.5% thanks to the purchasing of sustainable electricity and geothermal heat.Energy efficiency remained the same in 2017.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".